Parallel Feature Selection Inspired by Group Testing

NeurIPS 2014 Yingbo ZhouUtkarsh PorwalCe ZhangHung Q. NgoXuanlong NguyenChristopher RéVenu Govindaraju

This paper presents a parallel feature selection method for classification that scales up to very high dimensions and large data sizes. Our original method is inspired by group testing theory, under which the feature selection procedure consists of a collection of randomized tests to be performed in parallel... (read more)

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