Dimension reduction of microarray data based on local tangent space alignment

Abstract

We introduce the new nonlinear dimension reduction method: LTSA, in dealing with the difficulty of analyzing high-dimensional, nonlinear microarray data. Firstly, we analyze the applicability of the method and we propose the reconstruction error of LTSA. The method is tested on Iris data set and acute leukemias microarray data. The results show good visualization performance. And LTSA outperforms PCA on determining the reduced dimension. There is only subtle change in the clustering correctness after dimension reduction by LTSA. It is evident that application of nonlinear dimension reduction techniques could have a promising perspective in microarray data analysis.