DeepAI AI Chat
Log In Sign Up

Multi-view Orthonormalized Partial Least Squares: Regularizations and Deep Extensions

by   Li Wang, et al.
The University of Texas at Arlington

We establish a family of subspace-based learning method for multi-view learning using the least squares as the fundamental basis. Specifically, we investigate orthonormalized partial least squares (OPLS) and study its important properties for both multivariate regression and classification. Building on the least squares reformulation of OPLS, we propose a unified multi-view learning framework to learn a classifier over a common latent space shared by all views. The regularization technique is further leveraged to unleash the power of the proposed framework by providing three generic types of regularizers on its inherent ingredients including model parameters, decision values and latent projected points. We instantiate a set of regularizers in terms of various priors. The proposed framework with proper choices of regularizers not only can recast existing methods, but also inspire new models. To further improve the performance of the proposed framework on complex real problems, we propose to learn nonlinear transformations parameterized by deep networks. Extensive experiments are conducted to compare various methods on nine data sets with different numbers of views in terms of both feature extraction and cross-modal retrieval.


page 1

page 2

page 3

page 4


Multi-view Hybrid Embedding: A Divide-and-Conquer Approach

We present a novel cross-view classification algorithm where the gallery...

Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval

In this paper, the problem of multi-view embedding from different visual...

Multi-view Deep Subspace Clustering Networks

Multi-view subspace clustering aims to discover the inherent structure b...

Applications of Multi-view Learning Approaches for Software Comprehension

Program comprehension concerns the ability of an individual to make an u...

A Multi-View Dynamic Fusion Framework: How to Improve the Multimodal Brain Tumor Segmentation from Multi-Views?

When diagnosing the brain tumor, doctors usually make a diagnosis by obs...

Uncorrelated Semi-paired Subspace Learning

Multi-view datasets are increasingly collected in many real-world applic...

Joint Structured Models for Extraction from Overlapping Sources

We consider the problem of jointly training structured models for extrac...