  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.
