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Feature fusion using Canonical Correlation Analysis (CCA)

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# Feature fusion using Canonical Correlation Analysis (CCA)

Feature fusion is the process of combining two feature vectors to obtain a single feature vector, which is more discriminative than any of the input feature vectors. 
CCAFUSE applies feature level fusion using a method based on Canonical Correlation Analysis (CCA). It gets the train and test data matrices from two modalities X and Y, and consolidates them into a single feature set Z.

Details can be found in:

M. Haghighat, M. Abdel-Mottaleb, W. Alhalabi, "Fully Automatic Face Normalization and Single Sample Face Recognition in Unconstrained Environments," Expert Systems With Applications, vol. 47, pp. 23-34, April 2016.
http://dx.doi.org/10.1016/j.eswa.2015.10.047

(C)	Mohammad Haghighat, University of Miami
      	haghighat@ieee.org
      	PLEASE CITE THE ABOVE PAPER IF YOU USE THIS CODE.

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