Canonical correlation analysis (CCA) is one of the most well-known methods to extract features from multi-view data and has attracted much attention in recent years. However, classical CCA is unsupervised and does not take discriminant information into ...
As a powerful method for multi-view feature extraction, canonical correlation analysis (CCA) can find linear correlation relationship between feature sets from two views. However, CCA has two disadvantages. One is CCA cannot find nonlinear correlation ...
Canonical Correlation Analysis (CCA) is a technique that finds how "similar" are the subspaces that are spanned by the columns of two different matrices A έℜ(of size m-x-n) and B έℜ(of size m-x-l). CCA measures similarity by means of the cosines of the ...
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