Comparison of several learning subspace methods for classification

Several competition-based methods for classification are compared. Special attention is paid to subspace methods which are based on computing the projections of the patterns on the principal component vectors of the correlation matrices that span the pattern subspaces. A decision learning rule which updates the correlation matrices can be used to adjust the class boundary and improve the performance of the classification. A learning subspace method is proposed, and some other classification methods are reviewed. In this comparison, all of the methods are applied to a texture classification problem and the performance results are presented