Cited By
View all- Gunavathi CPremalatha K(2015)Cuckoo search optimisation for feature selection in cancer classificationInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2015.07209213:3(248-265)Online publication date: 1-Sep-2015
Sample-space-based feature extraction and class preserving projection for gene expression data
Author: 
Wenjun WangAuthors Info & Claims
Pages 224 - 246
Abstract
In order to overcome the problems of high computational complexity and serious matrix singularity for feature extraction using Principal Component Analysis PCA and Fisher's Linear Discrinimant Analysis LDA in high-dimensional data, sample-space-based feature extraction is presented, which transforms the computation procedure of feature extraction from gene space to sample space by representing the optimal transformation vector with the weighted sum of samples. The technique is used in the implementation of PCA, LDA, Class Preserving Projection CPP which is a new method for discriminant feature extraction proposed, and the experimental results on gene expression data demonstrate the effectiveness of the method.
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Published: 01 July 2013
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View all- Gunavathi CPremalatha K(2015)Cuckoo search optimisation for feature selection in cancer classificationInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2015.07209213:3(248-265)Online publication date: 1-Sep-2015
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