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A new and fast implementation for null space based linear discriminant analysis
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DOI：10.1016/j.patcog.2009.10.004.png)
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Abstract
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In this paper we present a new implementation for the null space based linear discriminant analysis. The main features of our implementation include: (i) the optimal transformation matrix is obtained easily by only orthogonal transformations without computing any eigendecomposition and singular value decomposition (SVD), consequently, our new implementation is eigendecomposition-free and SVD-free: (ii) its main computational complexity is from a economic QR factorization of the data matrix and a economic QR factorization of a n x n matrix with column pivoting, here n is the sample size, thus our new implementation is a fast one. The effectiveness of our new implementation is demonstrated by some real-world data sets. (C) 2009 Elsevier Ltd. All rights reserved.
Keywords:
Dimensionality reduction
Linear discriminant analysis
Null space based linear discriminant analysis
QR factorization
Singular value decomposition
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