| Paper: | MLSP-L1.1 | ||
| Session: | Pattern Recognition and Classification I | ||
| Time: | Thursday, May 20, 09:30 - 09:50 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification | ||
| Title: | A SEQUENTIAL APPROACH FOR MULTI-CLASS DISCRIMINANT ANALYSIS WITH KERNELS | ||
| Authors: | Fahed Abdallah; Université de Technologie de Troyes (UTT) | ||
| Cédric Richard; Université de Technologie de Troyes (UTT) | |||
| Régis Lengelle; Université de Technologie de Troyes (UTT) | |||
| 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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