Simultaneous Relevant Feature Identification and Classification in High-Dimensional Spaces

摘要

Molecular profiling technologies monitor thousands of transcripts, proteins, metabolites or other species concurrently in biological samples of interest. Given two-class, high-dimensional profiling data, nominal LIKNON is a specific implementation of a methodology for performing simultaneous relevant feature identification and classification. It exploits the well-known property that minimizing an l_1 norm (via linear programming) yields a sparse hyperplane. This work (ⅰ) examines computational, software and practical issues required to realize nominal LIKNON, (ⅱ) summarizes results from its application to five real world data sets, (ⅲ) outlines heuristic solutions to problems posed by domain experts when interpreting the results and (ⅳ) defines some future directions of the research.