Feature fusion using Discriminant Correlation Analysis (DCA)

Versión 1.0.1 (3,82 KB) por Mohammad Haghighat

https://github.com/mhaghighat/dcaFuse

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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.
DCAFUSE applies feature level fusion using a method based on Discriminant Correlation Analysis (DCA). It gets the train and test data matrices from two modalities X and Y, along with their corresponding class labels and consolidates them into a single feature set Z.

Details can be found in:

M. Haghighat, M. Abdel-Mottaleb, W. Alhalabi, "Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition," IEEE Transactions on Information Forensics and Security, vol. 11, no. 9, pp. 1984-1996, Sept. 2016.
http://dx.doi.org/10.1109/TIFS.2016.2569061

and

M. Haghighat, M. Abdel-Mottaleb W. Alhalabi, "Discriminant Correlation Analysis for Feature Level Fusion with application to multimodal biometrics," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 1866-1870.
http://dx.doi.org/10.1109/ICASSP.2016.7472000

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

Citar como

Haghighat, Mohammad, et al. “Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition.” IEEE Transactions on Information Forensics and Security, vol. 11, no. 9, Institute of Electrical and Electronics Engineers (IEEE), Sept. 2016, pp. 1984–96, doi:10.1109/tifs.2016.2569061.

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Más información sobre Dimensionality Reduction and Feature Extraction en Help Center y MATLAB Answers.

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Inspirado por: Dimensionality Reduction using Generalized Discriminant Analysis (GDA), Feature fusion using Canonical Correlation Analysis (CCA)

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dcaFuse

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Versión	Publicado	Notas de la versión
1.0.1	31 ene 2020	Updated the README
1.0.0.0	16 feb 2017	Added the references Added the references

Versión

Publicado

Notas de la versión

1.0.1

31 ene 2020

Updated the README

1.0.0.0

16 feb 2017

Added the references

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.

MLA	Haghighat, Mohammad, et al. “Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition.” IEEE Transactions on Information Forensics and Security, vol. 11, no. 9, Institute of Electrical and Electronics Engineers (IEEE), Sept. 2016, pp. 1984–96, doi:10.1109/tifs.2016.2569061.
APA	Haghighat, M., Abdel-Mottaleb, M., & Alhalabi, W. (2016). Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition. IEEE Transactions on Information Forensics and Security, 11(9), 1984–1996. Institute of Electrical and Electronics Engineers (IEEE). Retrieved from https://doi.org/10.1109%2Ftifs.2016.2569061
BibTeX	@article{Haghighat_2016, doi = {10.1109/tifs.2016.2569061}, url = {https://doi.org/10.1109%2Ftifs.2016.2569061}, year = 2016, month = {sep}, publisher = {Institute of Electrical and Electronics Engineers ({IEEE})}, volume = {11}, number = {9}, pages = {1984--1996}, author = {Mohammad Haghighat and Mohamed Abdel-Mottaleb and Wadee Alhalabi}, title = {Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition}, journal = {{IEEE} Transactions on Information Forensics and Security} }