Journal of Applied Mathematics and Decision Sciences
Volume 2005 (2005), Issue 3, Pages 165-175
A new approach to multiple class pattern classification with random matrices
School of Mathematics, Statistics, and Computer Science, Victoria University of Wellington, Wellington 6001, New Zealand
Received 8 April 2003; Revised 2 December 2003
Copyright © 2005 Dong Q. Wang and Mengjie Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We describe a new approach to multiple class pattern
classification problems with noise and high dimensional feature
space. The approach uses a random matrix which has a specified
distribution with mean and covariance matrix between any two columns of .
When is known, the maximum likelihood estimators
of the expectation , correlation , and covariance
can be obtained. The patterns with high dimensional
features and noise are then classified by a modified discriminant
function according to the maximum likelihood estimation results.
This new method is compared with a multilayer feed forward neural
network approach on nine digit recognition tasks of increasing
difficulty. Both methods achieved good results for those
classification tasks, but the new approach was more effective and
more efficient than the neural network method for difficult