On Dimensionality Reduction for Classification and Its Application.

In this paper, we evaluate the contribution of the classification constrained dimensionality reduction (CCDR) algorithm to the performance of several classifiers. We present an extension to previously introduced CCDR algorithm to multiple hypotheses. We investigate classification performance using the CCDR algorithm on hyperspectral satellite imagery data. We demonstrate the performance gain for both local and global classifiers and demonstrate a 10% improvement of the k-nearest neighbors algorithm performance. We present a connection between intrinsic dimension estimation and the optimal embedding dimension obtained using the CCDR algorithm