Résumé
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.
langue originale | Anglais |
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Pages (de - à) | V-453-V-456 |
journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
Volume | 5 |
état | Publié - 2004 |
Modification externe | Oui |
Evénement | Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada Durée: 17 mai 2004 → 21 mai 2004 |