WANG Wen-jun. New Method of Feature Extraction for Gene Expression Data Based on Class Preserving Projection[J]. Acta Electronica Sinica, 2012, 40(2): 358-364.
WANG Wen-jun. New Method of Feature Extraction for Gene Expression Data Based on Class Preserving Projection[J]. Acta Electronica Sinica, 2012, 40(2): 358-364. DOI: 10.3969/j.issn.0372-2112.2012.02.024.
is proposed from the point view of class relation of pairwise samples.Compared to LDA
CPP has the following two advantages.One is that the optimal subspace dimension is not restricted to the number of categories of samples
and the other is that computational complexity is lower.Experiments are performed on gene expression data for sample classification
and the results confirm the effectiveness of the method.Kernel CPP (KCPP) is presented by generalizing CPP to nonlinear space to solve the problem of nonlinear feature extraction
and the experiments on gene expression data verify the feasibility of the method.