A comparison of unsupervised dimension reduction algorithms for classification

Abstract

Distance preserving dimension reduction (DPDR) using the singular value decomposition has recently been introduced. In this paper, for disease diagnosis using gene or protein expression data, we present empirical comparison results between DPDR and other various dimension reduction (DR) methods (i.e. PCA, MDS, Isomap, and LLE) when using support vector machines with radial basis function kernel. Our results show that DPDR outperforms, as a whole, other DR methods in terms of classification accuracy, but at the same time, it gives significant efficiency compared with other methods since it has no parameter to be optimized. Based on these empirical results, we reach a promising conclusion that DPDR is one of the best DR methods at hand for modeling an efficient and distortion-free classifier for gene or protein expression data.