On Efficient Feature Ranking Methods for High-throughput Data Analysis
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On Efficient Feature Ranking Methods for High-throughput Data Analysis
Abstract— On Efficient Feature Ranking Methods for High-throughput Data Analysis. Efficient mining of high-throughput data has become one of the popular themes in the big data era. Existing biology related feature ranking methods mainly focus on statistical and annotation information. In this study, two efficient feature ranking methods are presented. Multi-target regression and graph embedding are < Final Year Projects 2016 > in an optimization framework, and feature ranking is achieved by introducing structured sparsity norm. Unlike existing methods, the presented methods have two advantages: (1) the feature subset simultaneously account for global margin information as well as locality manifold information. Consequently, both global and locality information are considered. (2) Features are selected by batch rather than individually in the algorithm framework.
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