Feature fusion using Canonical Correlation Analysis (CCA)

Versione 1.0.1 (3,02 KB) da Mohammad Haghighat

Feature level fusion using Canonical Correlation Analysis (CCA)

https://github.com/mhaghighat/ccaFuse

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Aggiornato 31 gen 2020

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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.

Cita come

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.

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AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Dimensionality Reduction and Feature Extraction >

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Ispirato da: Dimensionality Reduction using Generalized Discriminant Analysis (GDA)

Ispirato: Feature fusion using Discriminant Correlation Analysis (DCA)

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ccaFuse

Le versioni che utilizzano il ramo predefinito di GitHub non possono essere scaricate

Versione	Pubblicato	Note della release
1.0.1	31 gen 2020	Updated the references
1.0.0.0	12 mag 2016

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MLA	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.
APA	Haghighat, M., Abdel-Mottaleb, M., & Alhalabi, W. (2016). Fully automatic face normalization and single sample face recognition in unconstrained environments. Expert Systems with Applications, 47, 23–34. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.eswa.2015.10.047
BibTeX	@article{Haghighat_2016, doi = {10.1016/j.eswa.2015.10.047}, url = {https://doi.org/10.1016%2Fj.eswa.2015.10.047}, year = 2016, month = {apr}, publisher = {Elsevier {BV}}, volume = {47}, pages = {23--34}, author = {Mohammad Haghighat and Mohamed Abdel-Mottaleb and Wadee Alhalabi}, title = {Fully automatic face normalization and single sample face recognition in unconstrained environments}, journal = {Expert Systems with Applications} }