A sequential approach for multi-class discriminant analysis with kernels

Fahed Abdallah; Cédric Richard; Régis Lengelle

A sequential approach for multi-class discriminant analysis with kernels

Fahed Abdallah, Cédric Richard, Régis Lengelle

Research output: Contribution to journal › Conference article › peer-review

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 GDA method can arise both in the form of computational complexity and storage requirements. In this paper, we present a sequential algorithm for GDA avoiding these problems when one deals with large numbers of datapoints.

Original language	English
Pages (from-to)	V-453-V-456
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	5
Publication status	Published - 2004
Externally published	Yes
Event	Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada Duration: 17 May 2004 → 21 May 2004

Cite this

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title = "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 GDA method can arise both in the form of computational complexity and storage requirements. In this paper, we present a sequential algorithm for GDA avoiding these problems when one deals with large numbers of datapoints.",

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note = "Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing ; Conference date: 17-05-2004 Through 21-05-2004",

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AU - Abdallah, Fahed

AU - Richard, Cédric

AU - Lengelle, Régis

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Y1 - 2004

N2 - 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 GDA method can arise both in the form of computational complexity and storage requirements. In this paper, we present a sequential algorithm for GDA avoiding these problems when one deals with large numbers of datapoints.

AB - 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 GDA method can arise both in the form of computational complexity and storage requirements. In this paper, we present a sequential algorithm for GDA avoiding these problems when one deals with large numbers of datapoints.

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M3 - Conference article

AN - SCOPUS:4544328053

SN - 1520-6149

VL - 5

SP - V-453-V-456

JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

T2 - Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing

Y2 - 17 May 2004 through 21 May 2004

ER -

A sequential approach for multi-class discriminant analysis with kernels

Abstract

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