Canonical Correlation Analysis (CCA) Based Multi-View Learning: An Overview
This is an incremental contribution that organizes existing research for researchers in multi-view learning.
The paper addresses the lack of a comprehensive overview of Canonical Correlation Analysis (CCA)-based multi-view learning methods, which traditionally have limitations like linearity and lack of supervision, by providing a survey of representative extensions.
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.