Why direct LDA is not equivalent to LDA

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Why direct LDA is not equivalent to LDA
In this paper, we present counterarguments against the direct LDA algorithm (D-LDA), which was previously claimed to be equivalent to Linear Discriminant Analysis (LDA). We show from Bayesian decision theory that D-LDA is actually a special case of LDA by directly taking the linear space of class means as the LDA solution. The pooled covariance estimate is completely ignored. Furthermore, we demonstrate that D-LDA is not equivalent to traditional subspace-based LDA in dealing with the Small Sample Size problem. As a result, D-LDA may impose a significant performance limitation in general applications. 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Hui Gao, James W. Davis
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where PR
Authors Hui Gao, James W. Davis
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