Robust kernel discriminant analysis and its application to feature extraction and recognition

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Summary

A novel subspace method called robust kernel discriminant analysis is proposed for dimensionality reduction, which aims at finding a low-dimensional space of high-dimensional data by solving the eigenvalue problem.

Type
article
Published
2006-03-01
Cited by
10
References
13

Keywords

Kernel Fisher discriminant analysis, Dimensionality reduction, Subspace topology, Pattern recognition (psychology), Linear discriminant analysis

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