Adaptive discriminant analysis for microarray-based classification
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 01, 2008
- Source ID
- 10.1145/1342320.1342325
Entities
People
- Feng Liu
- Jennifer Neary
- Qi Tian
- Yijuan Lu
- Yufeng Wang
Organizations
- Army Research Office
- National Institute of General Medical Sciences
- National Institute on Minority Health and Health Disparities
- National Institutes of Health
- United States Department of Homeland Security
- University of Texas at San Antonio