Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 7, Issue 4, Pages 636-646Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2009.8
Keywords
High-dimensional data; feature selection; persistence; bias; convex optimization; primal-dual interior-point optimization; cancer classification; microarray gene analysis
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Funding
- Div Of Information & Intelligent Systems [0845951] Funding Source: National Science Foundation
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Extracting features from high-dimensional data is a critically important task for pattern recognition and machine learning applications. High-dimensional data typically have much more variables than observations, and contain significant noise, missing components, or outliers. Features extracted from high-dimensional data need to be discriminative, sparse, and can capture essential characteristics of the data. In this paper, we present a way to constructing multivariate features and then classify the data into proper classes. The resulting small subset of features is nearly the best in the sense of Greenshtein's persistence; however, the estimated feature weights may be biased. We take a systematic approach for correcting the biases. We use conjugate gradient-based primal-dual interior-point techniques for large-scale problems. We apply our procedure to microarray gene analysis. The effectiveness of our method is confirmed by experimental results.
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