A Survey on Multi-view Learning

TL;DR

This survey reviews various multi-view learning algorithms, categorizing them into co-training, multiple kernel learning, and subspace learning, highlighting their principles, differences, and potential for improved generalization over single-view methods.

Contribution

It provides a comprehensive classification and analysis of multi-view learning approaches, emphasizing the principles of consensus and complementarity, and discusses view construction and evaluation.

Findings

Multi-view learning often outperforms single-view learning in generalization.

Different approaches exploit either consensus or complementary principles.

Constructing and evaluating multiple views is crucial for effective multi-view learning.

Abstract

In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. In trying to organize and highlight similarities and differences between the variety of multi-view learning approaches, we review a number of representative multi-view learning algorithms in different areas and classify them into three groups: 1) co-training, 2) multiple kernel learning, and 3) subspace learning. Notably, co-training style algorithms train alternately to maximize the mutual agreement on two distinct views of the data; multiple kernel learning algorithms exploit kernels that naturally correspond to different views and combine kernels either linearly or non-linearly to improve learning performance; and subspace learning algorithms aim to obtain a latent subspace shared by multiple views by assuming that the input views are generated from this latent subspace. Though there is significant variance in the approaches to integrating multiple views to improve learning performance, they mainly exploit either the consensus principle or the complementary principle to ensure the success of multi-view learning. Since accessing multiple views is the fundament of multi-view learning, with the exception of study on learning a model from multiple views, it is also valuable to study how to construct multiple views and how to evaluate these views. Overall, by exploring the consistency and complementary properties of different views, multi-view learning is rendered more effective, more promising, and has better generalization ability than single-view learning.