Feature fusion using Discriminant Correlation Analysis (DCA)
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.
Zitieren als
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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- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Dimensionality Reduction and Feature Extraction >
- Signal Processing > Signal Processing Toolbox > Transforms, Correlation, and Modeling > Correlation and Convolution >
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Inspiriert von: Dimensionality Reduction using Generalized Discriminant Analysis (GDA), Feature fusion using Canonical Correlation Analysis (CCA)
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1.0.1 | Updated the README |
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1.0.0.0 | Added the references Added the references |
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