Complexity reduction for null space-based linear discriminant analysis

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In small sample size problems, the null space-based linear discriminant analysis (NLDA) provides a good discrimination performance but suffers from a complexity burden. Some schemes based on QR factorization and eigendecomposition have been proposed for complexity reduction. In this paper, we propose a scheme based on Cholesky decomposition for a further reduction of the complexity.
Publisher
IEEE
Issue Date
2011-08-25
Language
English
Citation

2011 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, pp.759 - 761

URI
http://hdl.handle.net/10203/173022
Appears in Collection
EE-Conference Papers(학술회의논문)

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