Sparse Feature Grouping based on

摘要

norm regularizer for the feature coefficients to make the learning model sparser. Moreover, the proposed algorithm adopts a grouping regularizer to encourage the feature coefficients in one group to be similar. To solve this problem, a new alternating direction optimization algorithm is proposed. Experiments are conducted on synthetic and real-life datasets, and the results show the comparative performance of the proposed method compared with several state-of-the-art methods.