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Conference papers
A sequential approach for multi-class discriminant analysis with kernels
Abstract : Linear discriminant analysis (LDA) is a standard statistical tool for data analysis. Recently, a method called generalized discriminant analysis (GDA) has been developed to deal with nonlinear discriminant analysis using kernel functions. Difficulties for the GDA method can arise in the form of both computational complexity and storage requirements. We present a sequential algorithm for GDA avoiding these problems when one deals with large numbers of datapoints.
https://hal-utt.archives-ouvertes.fr/hal-02290432
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Tuesday, September 17, 2019 - 4:18:32 PM
Last modification on : Friday, August 27, 2021 - 3:14:06 PM
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Tuesday, September 17, 2019 - 4:18:32 PM
Last modification on : Friday, August 27, 2021 - 3:14:06 PM
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