Feature fusion using Canonical Correlation Analysis (CCA)
Feature level fusion using Canonical Correlation Analysis (CCA)
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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.
Cite As
Haghighat, Mohammad, et al. “Fully Automatic Face Normalization and Single Sample Face Recognition in Unconstrained Environments.” Expert Systems with Applications, vol. 47, Elsevier BV, Apr. 2016, pp. 23–34, doi:10.1016/j.eswa.2015.10.047.
Acknowledgements
Inspired by: Dimensionality Reduction using Generalized Discriminant Analysis (GDA)
Inspired: Feature fusion using Discriminant Correlation Analysis (DCA)
Categories
Find more on Dimensionality Reduction and Feature Extraction in Help Center and MATLAB Answers
General Information
- Version 1.0.1 (3.02 KB)
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MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
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- Linux
Versions that use the GitHub default branch cannot be downloaded
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.1 | Updated the references |
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| 1.0.0.0 |
