Deep least squares fisher discriminant analysis
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Institute of Electrical and Electronics Engineers Inc. (IEEE)Date
2020-08-01Citation
10.1109/TNNLS.2019.2906302
IEEE Transactions on Neural Networks and Learning Systems 31.8 (2020): 2752-2763
ISSN
2162-237X (online); 2162-2388 (print)DOI
10.1109/TNNLS.2019.2906302Editor's Version
http://doi.org/10.1109/TNNLS.2019.2906302Subjects
Deep neural networks (DNNs); Fisher discriminant analysis (FDA); kernel discriminant analysis; nonlinear classifiers; InformáticaNote
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksRights
© IEEEAbstract
While being one of the first and most elegant tools for dimensionality reduction, Fisher linear discriminant analysis (FLDA) is not currently considered among the top methods for feature extraction or classification. In this paper, we will review two recent approaches to FLDA, namely, least squares Fisher discriminant analysis (LSFDA) and regularized kernel FDA (RKFDA) and propose deep FDA (DFDA), a straightforward nonlinear extension of LSFDA that takes advantage of the recent advances on deep neural networks. We will compare the performance of RKFDA and DFDA on a large number of two-class and multiclass problems, many of them involving class-imbalanced data sets and some having quite large sample sizes; we will use, for this, the areas under the receiver operating characteristics (ROCs) curve of the classifiers considered. As we shall see, the classification performance of both methods is often very similar and particularly good on imbalanced problems, but building DFDA models is considerably much faster than doing so for RKFDA, particularly in problems with quite large sample sizes
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Google Scholar:Diaz-Vico, David
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Dorronsoro Ibero, José Ramón
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