Sample-space-based feature extraction and class preserving projection for gene expression data

Authors:
Wenjun Wang
Affiliations:
School of Computer Science and Engineering, Xidian University, Post Box 161, #2 TaiBai Road, Xi'an, 710071, China
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2013

Citing 9
Cited 0

Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Discriminant Eigenfeatures for Image Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised cluster analysis for microarray data based on multivariate Gaussian mixture

Bioinformatics
Rapid and brief communication: Why direct LDA is not equivalent to LDA

Pattern Recognition
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Matrix factorisation methods applied in microarray data analysis

International Journal of Data Mining and Bioinformatics
Meta analysis algorithms for microarray gene expression data using Gene Regulatory Networks

International Journal of Data Mining and Bioinformatics
Using Gene Ontology to enhance effectiveness of similarity measures for microarray data

International Journal of Data Mining and Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

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.