Tumor gene expressive data classification based on locally linear representation fisher criterion

Authors:
Bo Li;Bei-Bei Tian;Jin Liu
Affiliations:
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China,Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Sys ...;School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China,Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Sys ...;State Key Lab. of Software Engineering, Wuhan, 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, a discriminant manifold learning method based on Locally Linear Embedding (LLE), which is named Locally Linear Representation Fisher Criterion (LLRFC), is proposed for the classification of tumor gene expressive data. In the proposed LLRFC, an inter-class graph and intra-class graph is constructed based on the class information of tumor gene expressive data, where the weights between nodes in both graph are optimized using locally linear representation trick. Moreover, a Fisher criterion is modeled to maximize the inter-class scatter and minimize the intra-class scatter simultaneously. Experiments on some benchmark tumor gene expressive data validate its efficiency.