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Complexity reduction for null space-based linear discriminant analysis

Complexity reduction for null space-based linear discriminant analysis,10.1109/PACRIM.2011.6032989,Hwang-Ki Min,Yuxi Hou,Iickho Song,Seungwon Lee,Hyun

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.
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