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The Ethereal
Canonical Correlation Analysis (CCA) Based Multi-View Learning: An Overview
July 03, 2019 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Chenfeng Guo, Dongrui Wu
arXiv ID
1907.01693
Category
cs.LG: Machine Learning
Cross-listed
cs.IR,
stat.ML
Citations
38
Venue
arXiv.org
Last Checked
6 months ago
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.
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