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A Sparse Learning Machine for High-Dimensional Data with Application to Microarray Gene Analysis
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DOI：10.1109/TCBB.2009.8.png)
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Abstract
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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.
Keywords:
High-dimensional data
feature selection
persistence
bias
convex optimization
primal-dual interior-point optimization
cancer classification
microarray gene analysis
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3.4
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3.3K
Citations:
6.4K
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