Building Class Sensitive Models for Tracking Applications

A method of generating complementary eigenspaces optimised for interclass and intra-class separability respectively is presented. The objective of creating these spaces is to improve the efficiency of eigenspace search algorithms. The inter-class optimised space may also be used to improve classification and a quantitative evaluation of this against conventional Principal Component Analysis and Canonical analysis (based on Linear Discriminant Analysis) is presented. A qualitative comparison of the intra-class optimised space and spaces produced by Principal Component Analysis on single class data is also presented. 1 Introduction Principal Component Analysis (PCA) is an efficient way of parameterising the variance within a multivariate data set such that the dimensionality may be reduced without greatly affecting approximation accuracy. This is done by finding the eigenvectors of a covariance matrix formed from the data set and forming an `eigenspace' based on these. In many ..