Locally Linear Discriminant Embedding for Tumor Classification

Authors:
Chun-Hou Zheng;Bo Li;Lei Zhang;Hong-Qiang Wang
Affiliations:
College of Information and Communication Technology, Qufu Normal University, Rizhao, China 276826 and Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of, Sciences, He ...;Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of, Sciences, Hefei, China 230031;Biometric Research Center, Dept. of Computing, Hong Kong Polytechnic University, Hong Kong, China;Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of, Sciences, Hefei, China 230031
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

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Abstract

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, a supervised version of locally linear embedding (LLE), named locally linear discriminant embedding (LLDE), is proposed for tumor classification. In the proposed algorithm, we construct a vector translation and distance rescaling model to enhance the recognition ability of the original LLE from two aspects. To validate the efficiency, the proposed method is applied to classify two different DNA microarray datasets. The prediction results show that our method is efficient and feasible.