Dimensionality reduction for microarray data using local mean based discriminant analysis

Authors:
Yan Cui;Chun-Hou Zheng;Jian Yang
Affiliations:
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China;College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, China;School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Year:
2013

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Abstract

In this paper we propose a new method for finding a low dimensional subspace of high dimensional microarray data. We developed a new criterion for constructing the weight matrix by using local neighborhood information to discover the intrinsic discriminant structure in the data. Also this approach applies regularized least square technique to extract relevant features. We assess the performance of the proposed methodology by applying it to four publicly available tumor datasets. In a low dimensional subspace, the proposed method classified these tumors accurately and reliably. Also, through a comparison study, the reliability of the dimensionality reduction and discrimination results is verified.