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Dimensionality Reduction for Microarray Data Using Local Mean Based Discriminant Analysis

  • Yan Cui
  • Chun-Hou Zheng
  • Jian Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7996)

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.

Keywords

Dimensionality reduction Discriminant analysis Gene expression Local mean 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yan Cui
    • 1
  • Chun-Hou Zheng
    • 2
  • Jian Yang
    • 1
  1. 1.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.College of Electrical Engineering and AutomationAnhui UniversityHefeiChina

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