
Unsupervised clustering under the Union of Polyhedral Cones (UOPC) model
In this paper, we consider clustering data that is assumed to come from one of finitely many pointed convex polyhedral cones. This model is referred to as the Union of Polyhedral Cones (UOPC) model. Similar to the Union of Subspaces (UOS) model where each data from each subspace is generated from a (unknown) basis, in the UOPC model each data from each cone is assumed to be generated from a finite number of (unknown) extreme rays.To cluster data under this model, we consider several algorithms  (a) Sparse Subspace Clustering by Nonnegative constraints Lasso (NCL), (b) Least squares approximation (LSA), and (c) Knearest neighbor (KNN) algorithm to arrive at affinity between data points. Spectral Clustering (SC) is then applied on the resulting affinity matrix to cluster data into different polyhedral cones. We show that on an average KNN outperforms both NCL and LSA and for this algorithm we provide the deterministic conditions for correct clustering. For an affinity measure between the cones it is shown that as long as the cones are not very coherent and as long as the density of data within each cone exceeds a threshold, KNN leads to accurate clustering. Finally, simulation results on real datasets (MNIST and YaleFace datasets) depict that the proposed algorithm works well on real data indicating the utility of the UOPC model and the proposed algorithm.
10/15/2016 ∙ by Wenqi Wang, et al. ∙ 0 ∙ shareread it

On Deterministic Conditions for Subspace Clustering under Missing Data
In this paper we present deterministic conditions for success of sparse subspace clustering (SSC) under missing data, when data is assumed to come from a Union of Subspaces (UoS) model. We consider two algorithms, which are variants of SSC with entrywise zerofilling that differ in terms of the optimization problems used to find affinity matrix for spectral clustering. For both the algorithms, we provide deterministic conditions for any pattern of missing data such that perfect clustering can be achieved. We provide extensive sets of simulation results for clustering as well as completion of data at missing entries, under the UoS model. Our experimental results indicate that in contrast to the full data case, accurate clustering does not imply accurate subspace identification and completion, indicating the natural order of relative hardness of these problems.
07/11/2016 ∙ by Wenqi Wang, et al. ∙ 0 ∙ shareread it

Topic Compositional Neural Language Model
We propose a Topic Compositional Neural Language Model (TCNLM), a novel method designed to simultaneously capture both the global semantic meaning and the local word ordering structure in a document. The TCNLM learns the global semantic coherence of a document via a neural topic model, and the probability of each learned latent topic is further used to build a MixtureofExperts (MoE) language model, where each expert (corresponding to one topic) is a recurrent neural network (RNN) that accounts for learning the local structure of a word sequence. In order to train the MoE model efficiently, a matrix factorization method is applied, by extending each weight matrix of the RNN to be an ensemble of topicdependent weight matrices. The degree to which each member of the ensemble is used is tied to the documentdependent probability of the corresponding topics. Experimental results on several corpora show that the proposed approach outperforms both a pure RNNbased model and other topicguided language models. Further, our model yields sensible topics, and also has the capacity to generate meaningful sentences conditioned on given topics.
12/28/2017 ∙ by Wenlin Wang, et al. ∙ 0 ∙ shareread it

Tensor Train Neighborhood Preserving Embedding
In this paper, we propose a Tensor Train Neighborhood Preserving Embedding (TTNPE) to embed multidimensional tensor data into low dimensional tensor subspace. Novel approaches to solve the optimization problem in TTNPE are proposed. For this embedding, we evaluate novel tradeoff gain among classification, computation, and dimensionality reduction (storage) for supervised learning. It is shown that compared to the stateofthearts tensor embedding methods, TTNPE achieves superior tradeoff in classification, computation, and dimensionality reduction in MNIST handwritten digits and Weizmann face datasets.
12/03/2017 ∙ by Wenqi Wang, et al. ∙ 0 ∙ shareread it

Wide Compression: Tensor Ring Nets
Deep neural networks have demonstrated stateoftheart performance in a variety of realworld applications. In order to obtain performance gains, these networks have grown larger and deeper, containing millions or even billions of parameters and over a thousand layers. The tradeoff is that these large architectures require an enormous amount of memory, storage, and computation, thus limiting their usability. Inspired by the recent tensor ring factorization, we introduce Tensor Ring Networks (TRNets), which significantly compress both the fully connected layers and the convolutional layers of deep neural networks. Our results show that our TRNets approach is able to compress LeNet5 by 11× without losing accuracy, and can compress the stateoftheart Wide ResNet by 243× with only 2.3% degradation in Cifar10 image classification. Overall, this compression scheme shows promise in scientific computing and deep learning, especially for emerging resourceconstrained devices such as smartphones, wearables, and IoT devices.
02/25/2018 ∙ by Wenqi Wang, et al. ∙ 0 ∙ shareread it

Principal Component Analysis with Tensor Train Subspace
Tensor train is a hierarchical tensor network structure that helps alleviate the curse of dimensionality by parameterizing largescale multidimensional data via a set of network of lowrank tensors. Associated with such a construction is a notion of Tensor Train subspace and in this paper we propose a TTPCA algorithm for estimating this structured subspace from the given data. By maintaining low rank tensor structure, TTPCA is more robust to noise comparing with PCA or TuckerPCA. This is borne out numerically by testing the proposed approach on the Extended YaleFace Dataset B.
03/13/2018 ∙ by Wenqi Wang, et al. ∙ 0 ∙ shareread it

A survey on Adversarial Attacks and Defenses in Text
Deep neural networks (DNNs) have shown an inherent vulnerability to adversarial examples which are maliciously crafted on real examples by attackers, aiming at making target DNNs misbehave. The threats of adversarial examples are widely existed in image, voice, speech, and text recognition and classification. Inspired by the previous work, researches on adversarial attacks and defenses in text domain develop rapidly. To the best of our knowledge, this article presents a comprehensive review on adversarial examples in text. We analyze the advantages and shortcomings of recent adversarial examples generation methods and elaborate the efficiency and limitations on countermeasures. Finally, we discuss the challenges in adversarial texts and provide a research direction of this aspect.
02/12/2019 ∙ by Wenqi Wang, et al. ∙ 0 ∙ shareread it

Synthetic Data Generation and Adaption for Object Detection in Smart Vending Machines
This paper presents an improved scheme for the generation and adaption of synthetic images for the training of deep Convolutional Neural Networks(CNNs) to perform the object detection task in smart vending machines. While generating synthetic data has proved to be effective for complementing the training data in supervised learning methods, challenges still exist for generating virtual images which are similar to those of the complex real scenes and minimizing redundant training data. To solve these problems, we consider the simulation of cluttered objects placed in a virtual scene and the wideangle camera with distortions used to capture the whole scene in the data generation process, and postprocessed the generated images with a elaboratelydesigned generative network to make them more similar to the real images. Various experiments have been conducted to prove the efficiency of using the generated virtual images to enhance the detection precision on existing datasets with limited real training data and the generalization ability of applying the trained network to datasets collected in new environment.
04/28/2019 ∙ by Kai Wang, et al. ∙ 0 ∙ shareread it
Wenqi Wang
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