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Paper details
Number 2 - June 2013
Volume 23 - 2013
Linear discriminant analysis with a generalization of the Moore–Penrose pseudoinverse
Tomasz Górecki, Maciej Łuczak
Abstract
The Linear Discriminant Analysis (LDA) technique is an important and well-developed area of classification, and to
date many linear (and also nonlinear) discrimination methods have been put forward. A complication in applying LDA
to real data occurs when the number of features exceeds that of observations. In this case, the covariance estimates
do not have full rank, and thus cannot be inverted. There are a number of ways to deal with this problem. In this
paper, we propose improving LDA in this area, and we present a new approach which uses a generalization of the
Moore–Penrose pseudoinverse to remove this weakness. Our new approach, in addition to managing the problem of inverting
the covariance matrix, significantly improves the quality of classification, also on data sets where we can invert the
covariance matrix. Experimental results on various data sets demonstrate that our improvements to LDA are efficient and
our approach outperforms LDA.
Keywords
linear discriminant analysis, Moore–Penrose pseudoinverse, machine learning