AAAI Publications, Thirtieth AAAI Conference on Artificial Intelligence

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Uncorrelated Group LASSO
Deguang Kong, Ji Liu, Bo Liu, Xuan Bao

Last modified: 2016-02-21


l2,1-norm is an effective regularization to enforce a simple group sparsity for feature learning. To capture some subtle structures among feature groups, we propose a new regularization called exclusive group l2,1-norm. It enforces the sparsity at the intra-group level by using l2,1-norm, while encourages the selected features to distribute in different groups by using l2 norm at the inter-group level. The proposed exclusivegroup l2,1-norm is capable of eliminating the feature correlationsin the context of feature selection, if highly correlated features are collected in the same groups. To solve the generic exclusive group l2,1-norm regularized problems, we propose an efficient iterative re-weighting algorithm and provide a rigorous convergence analysis. Experiment results on real world datasets demonstrate the effectiveness of the proposed new regularization and algorithm.


exclusive; lasso; group; feature selection

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