Constrained maximum variance mapping for tumor classification

Authors:
Chun-Hou Zheng;Feng-Ling Wu;Bo Li;Juan Wang
Affiliations:
College of Information and Communication Technology, Qufu Normal University, Rizhao, Shandong, China;College of Electrical Information and Automation, Qufu Normal University, Rizhao, Shandong, China;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Anhui, China;College of Information and Communication Technology, Qufu Normal University, Rizhao, Shandong, China
Venue:
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Feature extraction using constrained maximum variance mapping

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A supervised orthogonal discriminant projection for tumor classification using gene expression data

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Abstract

It is of great importance to classify the gene expression data into different classes. In this paper, followed the assumption that the gene expression data of tumor may be sampled from the data with a probability distribution on a sub-manifold of ambient space, an efficient feature extraction method named as Constrained Maximum Variance Mapping (CMVM), is presented for tumor classification. The proposed algorithm can be viewed as a linear approximation of multi-manifolds learning based approach, which takes the local geometry and manifold labels into account. The proposed CMVM method was tested on four DNA microarray datasets, and the experimental results demonstrated that it is efficient for tumor classification.