Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Discriminant Analysis for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Information Retrieval
Machine learning in DNA microarray analysis for cancer classification
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Customized prediction of respiratory motion with clustering from multiple patient interaction
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Microarray technology has generated enormous amounts of high-dimensional gene expression data, providing a unique platform for exploring gene regulatory networks. However, the curse of dimensionality plagues effort to analyze these high throughput data. Linear Discriminant Analysis (LDA) and Biased Discriminant Analysis (BDA) are two popular techniques for dimension reduction, which pay attention to different roles of the positive and negative samples in finding discriminating subspace. However, the drawbacks of these two methods are obvious: LDA has limited efficiency in classifying sample data from subclasses with different distributions, and BDA does not account for the underlying distribution of negative samples. In this paper, we propose a novel dimension reduction technique for microarray analysis: Adaptive Discriminant Analysis (ADA), which effectively exploits favorable attributes of both BDA and LDA and avoids their unfavorable ones. ADA can find a good discriminative subspace with adaptation to different sample distributions. It not only alleviates the problem of high dimensionality, but also enhances the classification performance in the subspace with naïve Bayes classifier. To learn the best model fitting the real scenario, boosted Adaptive Discriminant Analysis is further proposed. Extensive experiments on the yeast cell cycle regulation data set, and the expression data of the red blood cell cycle in malaria parasite Plasmodium falciparum demonstrate the superior performance of ADA and boosted ADA. We also present some putative genes of specific functional classes predicted by boosted ADA. Their potential functionality is confirmed by independent predictions based on Gene Ontology, demonstrating that ADA and boosted ADA are effective dimension reduction methods for microarray-based classification.