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Erschienen in:
2012 | OriginalPaper | Buchkapitel
A Neighborhood Preserving Based Semi-supervised Dimensionality Reduction Method for Cancer Classification
verfasst von : Xianfa Cai, Jia Wei, Guihua Wen, Jie Li
Erschienen in: Foundations of Intelligent Systems
Verlag: Springer Berlin Heidelberg
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Cancer classification of gene expression data helps determine appropriate treatment and the prognosis. Accurate prediction to the type or size of tumors relies on adopting efficient classification models such that patients can be provided with better treatment to therapy. In order to gain better classification, in this study, a linear relevant feature dimensionality reduction method termed the neighborhood preserving based semi-supervised dimensionality reduction (NPSSDR) is applied. Different from traditional supervised or unsupervised methods, NPSSDR makes full use of side information, which not only preserves the must-link and cannot-link constraints but also can preserve the local structure of the input data in the low dimensional embedding subspace. Experimental results using public gene expression data show the superior performance of the method.