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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.
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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 : Wednesday, August 31, 2022 - 6:55:53 PM


  • HAL Id : hal-02290432, version 1



Fahed Abdallah, Cédric Richard, Régis Lengellé. A sequential approach for multi-class discriminant analysis with kernels. 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2004, Montreal, Canada. pp.V-453-6. ⟨hal-02290432⟩



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