Canonical Correlation Analysis (CCA) Based Multi-View Learning: An Overview

TL;DR

This paper reviews various CCA-based multi-view learning methods, highlighting their evolution from traditional linear, unsupervised approaches to advanced nonlinear, supervised, and generalized extensions.

Contribution

It provides the first comprehensive overview of diverse CCA-based MVL techniques, summarizing their developments and differences.

Findings

Traditional CCA is limited to linear, unsupervised two-view data.

Many nonlinear and supervised CCA extensions have been proposed.

The paper categorizes and compares these approaches comprehensively.

Abstract

Multi-view learning (MVL) is a strategy for fusing data from different sources or subsets. Canonical correlation analysis (CCA) is very important in MVL, whose main idea is to map data from different views onto a common space with maximum correlation. Traditional CCA can only be used to calculate the linear correlation of two views. Besides, it is unsupervised and the label information is wasted. Many nonlinear, supervised, or generalized extensions have been proposed to overcome these limitations. However, to our knowledge, there is no overview for these approaches. This paper provides an overview of many representative CCA-based MVL approaches.