A Sparse Learning Machine for High-Dimensional Data with Application to Microarray Gene Analysis

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.