Multi-task support vector machines for feature selection with shared knowledge discovery

Abstract

Feature selection is an effective way to reduce computational cost and improve feature quality for the large-scale multimedia analysis system. In this paper, we propose a novel feature selection method in which the hinge loss function with a l2;1 norm regularization term is used to learn a sparse feature selection matrix for each learning task. Meanwhile… (More)
DOI: 10.1016/j.sigpro.2014.12.012

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