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.
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.
Compatibilità della release di MATLAB
Compatibilità della piattaforma
Windows macOS LinuxCategorie
- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Dimensionality Reduction and Feature Extraction >
Tag
Riconoscimenti
Ispirato da: Dimensionality Reduction using Generalized Discriminant Analysis (GDA)
Ispirato: Feature fusion using Discriminant Correlation Analysis (DCA)
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Scopri Live Editor
Crea script con codice, output e testo formattato in un unico documento eseguibile.
Le versioni che utilizzano il ramo predefinito di GitHub non possono essere scaricate
Versione | Pubblicato | Note della release | |
---|---|---|---|
1.0.1 | Updated the references |
|
|
1.0.0.0 |
|