Hierarchical Optimal Transport for Robust Multi-View Learning

06/04/2020 ∙ by Dixin Luo, et al. ∙ Duke University 0

Traditional multi-view learning methods often rely on two assumptions: (i) the samples in different views are well-aligned, and (ii) their representations in latent space obey the same distribution. Unfortunately, these two assumptions may be questionable in practice, which limits the application of multi-view learning. In this work, we propose a hierarchical optimal transport (HOT) method to mitigate the dependency on these two assumptions. Given unaligned multi-view data, the HOT method penalizes the sliced Wasserstein distance between the distributions of different views. These sliced Wasserstein distances are used as the ground distance to calculate the entropic optimal transport across different views, which explicitly indicates the clustering structure of the views. The HOT method is applicable to both unsupervised and semi-supervised learning, and experimental results show that it performs robustly on both synthetic and real-world tasks.



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1 Introduction

Multi-view learning seeks to represent data collected from different sources (, multi-view data) and fuse them in an unsupervised or semi-supervised manner. This learning strategy helps fully leverage the information in different views, which is beneficial for many real-world learning tasks, , predicting diseases based on multiple clinical testing records yuan2018multi ; zhang2018multi and embedding words semantically across different languages guo2012cross . Especially for predictive tasks with few labeled data (and possibly no labels in some views), multi-view learning methods impose useful regularization on target models, and accordingly assist mitigate over-fitting issues. However, traditional multi-view learning methods are built on two questionable assumptions, which may limit their practical application.

Firstly, most existing multi-view learning methods zhao2017multi ; li2018survey assume that their training data in different views are well-aligned. This requirement is inappropriate in many settings. For example, real-world multi-view learning often requires us to collect multi-view data from different organizations, , predicting credit level based on account balances from different banks, diagnosing diseases based on clinical and genetic reports from different hospitals, etc. For security and privacy, the multi-view data from different organizations are anonymous and shuffled before releasing. Moreover, it is likely that the views of the data collected by different organizations are generated by different groups of individuals. Therefore, real-world multi-view data are often independent, and thus, lack a clear correspondence. Secondly, the multi-view learning methods based on co-regularization strategies andrew2013deep ; quang2013unifying ; ding2014low ; wang2015deep ; benton2019deep assume that the latent representations of the data in different views obey the same distribution. In practice, however, the information in a view can be redundant for some views and complementary for the others. For such multi-view data, the views have a clustering structure, and thus, obey different latent distributions. Enforcing a single distribution across all the views may cause serious over-regularization problems.

Figure 1: An illustration of our hierarchical optimal transport model for robust multi-view learning. Here, denotes the encoder mapping the samples of the

-th view to the latent representation. The classifier takes the concatenation of the latent representations of different views as its input and predicts target labels. The encoders and the classifiers can be trained in a semi-supervised manner when some well-aligned labeled data are available. Based on the sliced Wasserstein distance between the latent representations of different views (the blue arrows) or that between the latent representations and the references (the orange arrows), we build two HOT models and learn optimal transport matrices to indicate the clustering structure of the views.

In this work, we propose a new multi-view learning method based on optimal transport theory, mitigating the dependency of multi-view learning on the aforementioned two assumptions. As illustrated in Figure 1, for the latent representations of different views, we leverage sliced Wasserstein distance bonneel2015sliced to measure the discrepancy between their distributions, which does not require the correspondence between samples. This modification is a generalization of a traditional co-regularization strategy. For each pair of views, we introduce a learnable weight to their sliced Wasserstein distance, and these weights can be interpreted as the optimal transport between different views. The optimal transport defined on the sliced Wasserstein distances leads to a hierarchical optimal transport (HOT) model. It provides a new regularization strategy, that represents the pairwise similarity between different views and implicitly indicates their clustering structure. Furthermore, given some learnable global representations as references (the orange distributions in Figure 1), we can apply this HOT model to find the clustering structure of the views explicitly (, the orange optimal transport matrix in Figure 1). We learn this HOT model efficiently by combining mini-batch gradient descent kingma2014adam with the Sinkhorn scaling algorithm cuturi2013sinkhorn . The proposed method achieves robust multi-view learning with fewer assumptions, and is demonstrated to perform well on multiple datasets.

2 Proposed Model

Suppose that we have a set of samples collected from views, , for , where is the sample space of the -th view, for is a -dimensional sample in the space, and contains observed samples. We aim to learn encoders to extract latent representations for the views, and leverage these representations as features for various learning tasks. Denote the encoder of the -th view as , where is the -dimensional latent space of view .

Multi-view learning achieves the desired aim by learning the encoders jointly. We focus on the multi-view learning strategy called co-regularization, that leverages the information of one view to impose constraints on the others. Typical co-regularization methods include canonical correlation analysis (CCA) chaudhuri2009multi and its variants via2007learning ; white2012convex ; andrew2013deep ; ding2014low ; wang2015deep ; benton2019deep . These methods assume that there is a common -dimensional latent space shared by the outputs of the encoders. The projections of the encoders’ outputs to this space obey the same distribution, or their distributions are highly correlated with each other. For example, the Least Squares based Generalized CCA (LSCCA) via2007learning learns the encoders by


where is a matrix projecting the latent representations of each view to a common latent space, and

is an identity matrix. The objective function of LSCCA is the summation of the pairwise comparisons between different views, which penalizes differences between them. Similarly, we also learn global latent representations shared by all the views, as for Generalized Deep CCA (GDCCA) 

benton2019deep :


where contains the global latent representations. The GDCCA compares different views in an indirect way – taking as a reference, it makes the latent representations of all the views approach and suppresses the difference between different views.

Semi-supervised Learning. The above multi-view learning methods can be used as regularizers in semi-supervised learning. In particular, when some multi-view data are labeled, we can learn a classifier associated with the encoders by solving the following optimization problem:111We ignore the constraints associated with to simplify notation.


where and are the sets of indices for labeled and unlabeled data, respectively; is the label associated with the -th multi-view data point; and is a classifier taking the concatenation of as its input. The first term in (3) can be the cross entropy loss for labeled data. The second term can be an arbitrary regularizer imposed on all the views, which can be implemented as the objective functions in (1, 2). Finally, the last term can be any additional regularizer imposed on each single view. We can implement this term as the manifold-based regularizer that encourages the smoothness of the data manifold quang2013unifying ; sindhwani2008rkhs . Alternatively, we can introduce learnable decoders associated with the encoders to construct autoencoders and implement

as the reconstruction loss between the sample in each view and its estimation 

wang2019adversarial ; huang2018multimodal ; ye2016learning ; wang2015deep . The two regularizers are weighted by and .

2.1 Sliced Wasserstein distance for view matching

The multi-view learning methods in (1, 2) require that the samples in different views are well-aligned, , for is sampled jointly from .222Besides the co-regularization strategy, other multi-view learning strategies like co-training and multi-kernel fusion are also dependent on well-aligned multi-view data, as discussed in Section 4 When the samples in each view are generated independently and only a few samples are labeled and well-aligned, as shown in Figure 1, we need to design a new regularizer to achieve robust multi-view learning without correspondence. A natural way to modify the objective functions in (1, 2) is by introducing permutation matrices to match the samples, , replacing the terms in the objective functions with


where represents the set of all valid permutation matrices.333Without loss of generality, here we assume that for different views, their number of samples are the same. Therefore, the ’s in (4, 5) are permutation matrices. The in (4) is the permutation matrix indicating the correspondence between the samples of the -th view and those of the -th view, and the in (5) is the permutation matrix indicating the correspondence between the samples of the -th view and the global latent representation.

Because such matching problems are NP-hard, we propose an approximate algorithm to solve them efficiently based on the sliced Wasserstein distance bonneel2015sliced ; kolouri2018sliced .

Definition 2.1 (Sliced Wasserstein).

Let be the -dimensional hypersphere and the uniform measure on . For each , we denote the projection on as , where

. For arbitrary two probability measures defined on a compact metric space

, denoted as and , we define their sliced Wasserstein distance as


where is the one-dimensional (1D) distribution after the projection, and is the Wasserstein distance between and defined on .

The sliced Wasserstein distance provides a valid metric to measure the discrepancy between different distributions. Given the samples of these two distributions, , and , we can calculate the sliced Wasserstein distance empirically as


where contains projectors randomly selected from , and is the empirical estimation of the Wasserstein distance between the two 1D distributions and . As shown in (7), when calculating , we do not need to learn permutation matrices explicitly – for , we just need to sort and

in ascending (or descending) order, and calculate the Euclidean distance between the sorted vectors 

kolouri2018sliced .

Denote the representations of each view’s samples in the common latent space (, ) as . These latent representations can be viewed as the samples of an unknown conditional distribution . From this standpoint, the matching problems in (4, 5) empirically measure the discrepancy between different conditional distributions, which can be replaced approximately by the sliced Wasserstein distance in (7). In theory, we have

Proposition 2.2.

Given two sets of samples, denoted as and , each of which has -dimensional samples, .

The proof of this proposition is found in the Supplementary Material. This result indicates that the sliced Wasserstein distance achieves a lower bound of the optimal objective functions in (4, 5). Therefore, to match the views based on their unaligned samples, we plug the sliced Wasserstein distance into (1, 2) and obtain the following two models:


2.2 Hierarchical optimal transport for view clustering

The new objective functions in (8, 9) do not require well-aligned samples, but they still tend to make the latent representations of different views approach the same distribution. In particular, (8) penalizes the sliced Wasserstein distance between each pair of views, while (9) penalizes the sliced Wasserstein distance between each view and the reference . To overcome this problem, we further modify the multi-view learning methods as follows. For (8), we introduce learnable weights to the sliced Wasserstein distances and obtain


where represents a -dimensional all-one vector and is the matrix of the weights. To avoid trivial solutions (, ), we restrict to be (

) a doubly stochastic matrix, and (

) a symmetric matrix with all-zero diagonal elements. By solving this problem, we find the clustering structure of the views implicitly – the views corresponding to the pairs with large weights belong to the same clusters. Note that in (10) we relax the strict constraint to a least squares based regularizer, which helps us to apply mini-batch gradient descent directly to learn the model. In the subsequent experiments, we find that this relaxation does not do harm to the learning results while simplifies our learning algorithm.

For (9), besides introducing learnable weights, we consider multiple global latent representations which correspond to different clusters directly. The problem becomes


where is the number of clusters we set for the views, which is fixed as 3 in the following experiments; represents the global latent representation matrix corresponding to the -th cluster; and

is the matrix of the weights, which is restricted as a doubly stochastic matrix. According to its constraints, we can explain the matrix as the joint distribution of the views and the clusters, and the element

is the probability that the -th view belongs to the -th cluster. In other words, this method can find the clustering structures explicitly. Similar to (10), we relax the strict constraint to a regularizer in (11).

In both these two methods, we establish an optimal transport model with a hierarchical architecture. The in (10) achieves an optimal transport across different views, whose ground distance is the sliced Wasserstein distance between the latent representations of the views. Similarly, the in (11) is an optimal transport from the views to their clusters, whose ground distance is the sliced Wasserstein distance between the latent representations of the views to those of the clusters. These optimal transport matrices can be learned efficiently by computing the entropic Wasserstein distance based on the Sinkhorn scaling algorithm cuturi2013sinkhorn . To our knowledge, our work is the first to leverage the hierarchical optimal transport model to implement multi-view learning methods. This framework provides a new way to represent different views and find their clustering structure.

1:  Initialize .
2:  For

each epoch:

3:   Sample batches from the views.
4:   Calculate .
5:   Calculate .
6:   Learn by the Sinkhorn algorithm cuturi2013sinkhorn .
7:   Fix

and calculate the loss function

as +.
8:   Update the model by Adam kingma2014adam .
Algorithm 1 The optimization of (10)
1:  Initialize .
2:  Initialize as random matrices.
3:  For each epoch:
4:   Sample batches from the views.
5:   Calculate .
6:   Learn by the Sinkhorn algorithm cuturi2013sinkhorn .
7:   Fix and calculate the loss function as .
8:   Update the model by Adam kingma2014adam .
Algorithm 2 The optimization of (11)

3 Learning Algorithm

We propose an efficient learning algorithm to solve the problems in (10, 11), based on alternating optimization. In each iteration, we first calculate the sliced Wasserstein distances and update the weight matrix via the Sinkhorn scaling algorithm cuturi2013sinkhorn . Then, we fix the weight matrix and learn the encoders and their projection matrices via mini-batch gradient descent, , the Adam algorithm kingma2014adam . Algorithms 1 and 2 show the details of our implementation, where is the set of doubly stochastic matrices with marginals and , and is the weight of the entropic regularizer when applying the Sinkhorn algorithm. The details of the Sinkhorn algorithm can be found in cuturi2013sinkhorn and in our Supplementary Material. In line 6 of Algorithm 1, we set the cost matrix to , to ensure for . Additionally, the Sinkhorn algorithm can make converge to a symmetric matrix when the cost matrix is symmetric and the marginals of are the same. Therefore, all the constraints on can be readily satisfied. In Algorithm 2, the in line 2 are initialized as Gaussian random matrices, and for each the number of columns is equal to the batch size. Moreover, when some well-aligned labeled data are available, we can apply these two algorithms to achieve semi-supervised learning. Considering the labeled data, we just need to replace the loss in Algorithms 1 and 2 with the loss in (3) and update the model and a classifier jointly.

Our HOT model is a new member of the hierarchical optimal transport family, combining sliced Wasserstein distance with entropic Wasserstein distance. Compared with existing hierarchical optimal transport models chen2018optimal ; lee2019hierarchical ; yurochkin2019hierarchical ; xu2020learning , our model has advantages from the perspectives of computational complexity and model flexibility. Given views, each of which contains samples in a batch, the computational complexity of our method is for Algorithm 1 and for Algorithm 2. Here, is the number of random projections used to compute a sliced Wasserstein distance, which is much smaller than , is the number of iterations used in the Sinkhorn scaling algorithm, and is the number of clusters for the views. The first term () corresponds to calculating the cost matrix based on sliced Wasserstein distance, and the second term () corresponds to computing the entropic Wasserstein distance based on the Sinkhorn scaling algorithm. Instead of using sliced Wasserstein distance, existing hierarchical optimal transport models apply Wasserstein distance chen2018optimal ; yurochkin2019hierarchical ; xu2020learning or entropic Wasserstein distance lee2019hierarchical to calculate the cost matrix . As a result, for each element of the cost matrix, the computational complexity is

when applying linear programming to compute Wasserstein distance, or

when applying Sinkhorn scaling algorithm to compute entropic Wasserstein distance lee2019hierarchical ; yurochkin2019hierarchical . To avoid these computations, such methods have to assume the distribution of the samples to be Gaussian chen2018optimal ; xu2020learning , which limits their applicability and increases the risk of over-regularization. According to the analysis above, our learning algorithms has much less computational complexity, without the need to impose any assumptions on the latent distributions of the views.

4 Related Work

Multi-view learning Multi-view learning can be broadly categorized into three strategies zhao2017multi ; sun2013survey : co-training, multi-kernel fusion, and co-regularization guo2019canonical . Co-training requires () that the views in the training data are conditionally independent, and () that each view is sufficient to predict labels. It iteratively learns a separate classifier for each view using labeled samples and annotates the unlabeled data based on the most confident predictions of each classifier kumar2011co ; ma2017self . Kernel-based methods merge the kernel matrices of different views and learn global representations based on the merged kernel de2010multi ; li2015large . Co-regularization methods add regularization terms to encourage the data from different views to be consistent. The representative regularizers include () CCA-based methods guo2019canonical ; chaudhuri2009multi ; sindhwani2008rkhs ; via2007learning ; guo2012cross ; andrew2013deep that penalize the difference between the views in the latent space, and () linear discriminate analysis based methods jin2014multi that require labeled data. Generally, the co-training methods are not scalable for cases with more than two views, and the kernel-based methods are transductive. Because both approaches are not suitable for large-scale multi-view learning tasks, in our work we focus on the co-regularization strategy and its improvements. Additionally, all methods discussed above require well-aligned multi-view data. Although some methods have been proposed to achieve multi-view learning based on incomplete or noisy views xu2015multi ; jin2014multi ; christoudias2008multi , they rely on labeled data, which are not available in many scenarios.

Optimal transport-based learning Optimal transport theory villani2008optimal has proven to be useful in distribution matching memoli2011gromov ; su2017order , data clustering agueh2011barycenters ; xu2018distilled ; yurochkin2019hierarchical , and learning a generative model arjovsky2017wasserstein . Given two sets of samples, we can calculate the optimal transport between them by linear programming kusner2015word . With the help of the Sinkhorn algorithm benamou2015iterative , an entropic Wasserstein distance has been proposed to accelerate the computation of optimal transport cuturi2013sinkhorn . When only the distance between distributions is needed, one can apply the dual form of Wasserstein distance arjovsky2017wasserstein or the sliced Wasserstein distance bonneel2015sliced ; kolouri2018sliced to approximate the distance, and avoid explicitly computing the optimal transport. Recently, hierarchical optimal transport models have been proposed to compare the distributions with structural information, , the nonlinear factorization models in xu2018distilled ; xu2020gromov ; schmitzer2013hierarchical , and optimal transport models for multi-modal distributions chen2018optimal ; lee2019hierarchical ; yurochkin2019hierarchical . These hierarchical optimal transport models achieve encouraging performance on multi-modal distribution matching chen2018optimal ; lee2019hierarchical ; xu2020learning and data clustering yurochkin2019hierarchical ; xu2020gromov . Compared with existing HOT models, our model has lower computational complexity, and it does not have constraints on the target distributions.

Dataset # Samples # Cls. View 1/ View 2/ View 3/ View 4/ View 5/ View 6/
Caltech-7/20 1474/2386 7/20 Gabor/48 WM/40 CENTRIST/254 HOG/1984 GIST/512 LBP/928
Handwritten 2000 10 Pixel/240 Fourier/76 FAC/216 ZER/47 KAR/64 MOR/6
Table 1: The statistics of each real-world dataset

5 Experiments

To demonstrate the usefulness of the proposed multi-view learning methods, we test them on both synthetic and real-world datasets, with comparisons to state-of-the-art methods. For each method, we consider the following four datasets: Caltech7, Caltech20, and the Handwritten datasets in li2015large

. The Caltech7, Caltech20, and Handwritten datasets correspond to three image classification tasks. Each contains 5-6 kinds of visual features extracted by classic methods. The details of the feature extraction methods are provided at

https://github.com/yeqinglee/mvdata. The statistics of these datasets are summarized in Table 1. For each dataset, we test our method and the alternative approaches in 20 trials. In each trial, we randomly select 60% of the samples for training, 20% of the samples for validation, and the remaining 20% of samples for testing. For the training data, we keep 5% as well-aligned and labeled data. For existing multi-view learning methods that require well-aligned data, we just remove the labels of the remaining training data. Concerning the proposed robust multi-view learning methods and their variants, we randomly permute the remaining training data in each view and remove their labels to generate unaligned unlabeled data.

  Method Data Caltech7 Caltech20 Handwritten
  Baseline LSCCA via2007learning Aligned 87.361.43 71.202.74 87.983.46
DGCCA benton2019deep Aligned 87.601.08 71.802.61 87.123.89
AECCA wang2015deep AE Aligned 87.621.47 71.502.84 88.533.22
  Ours SW (8) Unaligned 87.111.55 70.943.05 88.583.29
SW (9) Unaligned 87.551.79 72.502.41 89.782.98
HOT (10) Unaligned 88.311.56 72.962.69 89.953.52
HOT (11), Unaligned 88.291.87 73.432.70 90.052.62
SW (8) AE Unaligned 87.491.25 70.942.73 89.903.31
SW (9) AE Unaligned 87.241.64 72.362.27 89.223.28
HOT (10) AE Unaligned 88.471.52 73.182.40 90.373.00
HOT (11), AE Unaligned 88.982.15 73.482.08 91.072.55
Table 2:

Averaged classification accuracy (%) and standard deviation (semi-supervised learning)

  Method Data Caltech7 Caltech20 Handwritten
  Baseline LSCCA via2007learning Aligned 82.331.84 60.422.16 70.207.77
DGCCA benton2019deep Aligned 75.564.59 54.673.13 66.155.20
AECCA wang2015deep AE Aligned 84.391.71 66.752.47 83.304.83
  Ours SW (8) Unaligned 82.251.47 59.933.74 65.679.80
SW (9) Unaligned 85.711.33 69.212.51 86.402.71
HOT (10) Unaligned 82.891.32 60.853.31 67.657.56
HOT (11), Unaligned 86.271.79 71.072.29 87.122.10
SW (8) AE Unaligned 83.881.70 67.272.96 82.733.70
SW (9) AE Unaligned 86.641.57 69.292.37 87.633.08
HOT (10) AE Unaligned 83.981.55 67.412.32 83.853.84
HOT (11), AE Unaligned 87.321.33 69.482.53 87.503.15
Table 3: Averaged classification accuracy (%) and standard deviation (unsupervised)
Figure 2: Visualizations of the optimal transport matrices for the Caltech7 (left) and the Handwritten (right).
Figure 3: Classification accuracy (%) after removing some views from the datasets
 Caltech7 HOT (11) Handwritten HOT (11)
 All 86.271.79 All 87.122.10
Gabor 86.031.90 Pix 84.453.17
WM 86.151.69 Fou 86.453.08
CENTRIST 86.011.21 FAC 84.652.57
HOG 82.952.12 ZER 84.402.15
GIST 85.871.10 KAR 84.753.38
LBP 85.432.84 MOR 79.302.58
  • “All” means training models using all the views.

  • View” means removing the data of the view and training models accoridngly.

5.1 Comparisons on semi-supervised and unsupervised learning

Applying the semi-supervised framework proposed in (3), we test different multi-view learning methods and record their classification accuracy. For each method, we train

multi-layer perceptron (MLP) models as encoders and a softmax layer as a classifier. To achieve fairness in these comparisons, all methods apply models with the same architecture and the same hyperparameters. In particular, we set the hyperparameters empirically as follows: the number of epochs is

; the learning rate is fixed as ; the batch size is ; for each , the dimension of its output is ; the dimension of the common latent space is ; in (3), and ; in (1011), ; for the Sinkhorn algorithm, the number of iterations is and ; for sliced Wasserstein distance, the number of projections is set to be in our methods. The robustness of our learning algorithms to the key hyperparameters above can be found in the Supplementary Material. The average performance of these methods for the 20 trials is reported in Table 2. The baselines include the LSCCA via2007learning , the DGCCA benton2019deep , and the autoencoder-assisted CCA (AECCA) wang2015deep . Compared with these baselines, which require well-aligned training data, our methods and their variants apply unaligned training data but achieve at least comparable performance on classification accuracy. Moreover, among our methods, applying our hierarchical optimal transport model generally achieves higher accuracy than applying sliced Wasserstein distance directly. These phenomena demonstrate the feasibility of sliced Wasserstein distance in multi-view learning and the advantage of our HOT model. Additionally, we can introduce a set of decoders corresponding to the encoders, and construct the regularizer as the reconstruction loss of the autoencoder (AE), which helps further improve the classification accuracy.

Besides semi-supervised learning, we can learn the latent representations of different views in an unsupervised manner, and then train a classifier based on them. The performance of different methods is shown in Table 3, which helps us evaluate the power of different methods on unsupervised feature extraction. Compared with the performance achieved by semi-supervised learning, the performance of the baselines drops precipitously in this setting. For the proposed methods, those depending on pairwise comparisons between views (, implementing (8, 10)) suffer from the degradation of performance as well, while those learning clusters of views explicitly (, implementing (9, 11)) retain high classification accuracy, which implies that learning the clustering structure of views explicitly might be more suitable for unsupervised multi-view learning. Again, in this experiment applying our hierarchical optimal transport model can improve the performance.

5.2 Justification of view clustering

According to Table 3, we find that applying our HOT model in (11) achieves encouraging performance, which implies that the clustering structure of views learned by this method is reasonable. In Figure 3, we visualize the corresponding optimal transport matrices learned for different datasets. We can find that for the views in the Caltech7 dataset, “Gabor” and “CENTIST” belong to one cluster while “WM” and “GIST” belong to another, and “HOG” and “LBP” corresponds to the mixture of the cluster 2 and 3. To verify the rationality of this clustering structure, we evaluate the significance of different views in our learning tasks by removing each of the views and training the model accordingly. As shown in Table 3, compared with the result achieved by using all views, removing either “Gabor” or “CENTRIST” (either “WM” or “GIST”) just degrades the classification accuracy slightly, while removing “HOG” or “LBP” does harm to the accuracy severely. This result demonstrates that the clusters we find indeed group the views with redundant information and comparable contributions. Similarly, for the views in the Handwritten dataset, the distribution of “Pix” on the three clusters is similar to those of “Fac” and “KAR”. Therefore, removing one of them from the training views leads to similar classification accuracy. On the other hand, “MOR” is a unique view belonging to the cluster 1, so removing it leads to serious degradation on the performance.

6 Conclusion

We have proposed a hierarchical optimal transport model to achieve robust multi-view learning. This method neither depends on the correspondence between the samples of different views nor requires the views to obey the same latent distribution. The proposed approach consistently outperforms many strong baseline models on multiple datasets, demonstrating its potential for complicated learning tasks in real-world scenarios. In the future, we plan to explore the practical applications of our method, , introducing it to federated learning and achieving some predictive tasks for financial and healthcare data analysis. Additionally, we would like to extend our method to multi-view multi-task learning.

7 Broader Impact Statement

This paper makes a significant contribution to extending the frontier of multi-view learning with fewer assumptions and better interpretability. To the best of our knowledge, our method is the first approach to learn latent representations for different views without correspondence and explore the clustering structure of the views at the same time. Its relationship to traditional multi-view learning methods is clarified in the paper as well. A potential application scenario of our work is multi-view learning based on distributed private data, , patients’ records in different hospitals, and individuals’ financial statements in different banks. Our method provides a potential solution to allow different organizations to share their data with better protections on data privacy: ) the data can be not only anonymous but also from different individuals; ) in the training phase, we don’t need to learn their correspondence explicitly. These advantages greatly reduce the risk of information leaking.


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8 Supplementary Material

8.1 The proof of Proposition 2.2

Proposition 2.2 Given two sets of samples, denoted as and , each of which has -dimensional samples, .


Given two sets of samples, denoted as and , each of which has -dimensional samples, we have


Here, . Because each , we have . ∎

8.2 The Sinkhorn scaling algorithm

The scheme of the Sinkhorn scaling algorithm is shown below:

1:  Initialize ,
2:  .
3:  for
4:   Sinkhorn iteration:     
5:  Return
Algorithm 3

8.3 The configuration of models and hyperparameters

We implement all the models with PyTorch and train them on a single NVIDIA GTX 1080 Ti GPU. For our methods, the hyperparameters are set empirically as follows: the number of epochs is

; the learning rate is fixed as ; the batch size is ; for each , the dimension of its output is 20; the dimension of the common latent space is 10; in (3), and ; in (1011), ; for the Sinkhorn algorithm, the number of iterations is and ; for sliced Wasserstein distance, the number of projections is set to be in our methods; for the HOT in (11), the number of the clusters of views is set to be .

Among these hyperparameters, there are three key hyperparameters: the batch size, the number of projections when calculating the sliced Wasserstein distance, and the number of clusters . In particular, the sliced Wasserstein distance used in our work provides an empirical estimation for the expected distance between distributions based on their samples. The batch size controls the number of samples used to calculate the sliced Wasserstein distance. The number of projections controls the precision and the stability of the estimation. Generally, using a large batch size and a large number of projections provides us better estimation but increases computations at the same time. In Figure 4(a) and Figure 4(b), we can find that the performance of our multi-view learning method (the HOT in (11)) is relatively robust to the change of these two hyperparameters. According to these two figures, we set the batch size to be 400 and the number of projections be 3. Similarly, Figure 4(c) shows that our method is robust to the number of clusters we set, and the best performance is achieved when . For semi-supervised learning, there are two more key hyperparameters: the weight of the multi-view learning regularizer and the weight in (10, 11). According to Figure 5, when and are set to be our method can achieve encouraging performance.

(a) Accuracy v.s. batch size
(b) Accuracy v.s. # projections
(c) Accuracy v.s. # clusters
Figure 4: The robustness of our method (the HOT in (11

)) to its hyperparameters in unsupervised learning. We use the Handwritten dataset in this experiment.

(a) Accuracy v.s.
(b) Accuracy v.s.
Figure 5: The robustness of our method (the HOT in (11)) to its hyperparameters in semi-supervised learning. We use the Handwritten dataset in this experiment.

8.4 More results of optimal transport matrices

Figure 6 visualize the optimal transport matrices obtained for different datasets when we learn the latent representations of their views in unsupervised ways. We can find that both of our two HOT models can predict the clusters of the views. However, the clustering structures learned by them are inconsistent in some cases. For example, for the Caltech7 dataset, “Gabor”, “WM”, and “HOG” are likely to be in the same cluster when we apply the HOT model in 10. On the other hand, when we use the HOT model in 11, “Gabor” is more likely to be grouped with “CENTRIST”. Because the performance of the second model is better than that of the first one for unsupervised learning, we think the clusters detected by the second model is more reliable, which is also verified in the Table 3 in the main paper.

Additionally, we find that for the simple tasks (, the Caltech7 and the Handwritten), our methods can learn sparse optimal transport matrices and detect clusters clearly. For complicated tasks, , those with many classes (the Caltech20), the optimal transport matrices we learned are often dense, and the clusters are not so obvious as those in the simple cases.

(a) The Caltech7
(b) The Caltech20
(c) The Handwritten
Figure 6: The visualizations of the optimal transport matrices for the views. In each subfigure, the left is the optimal transport matrix leaned by the HOT in (10), and the right is that learned by solving (11).