On the equivalent of low-rank linear regressions and linear discriminant analysis based regressions
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Exact top-k feature selection via l2,0-norm constraint
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Co-regularized ensemble for feature selection
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Adaptive loss minimization for semi-supervised elastic embedding
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Social trust prediction using rank-k matrix recovery
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Feature selection is an essential component of data mining. In many data analysis tasks where the number of data point is much less than the number of features, efficient feature selection approaches are desired to extract meaningful features and to eliminate redundant ones. In the previous study, many data mining techniques have been applied to tackle the above challenging problem. In this paper, we propose a new $\ell_{2,1}$-norm SVM, that is, multi-class hinge loss with a structured regularization term for all the classes to naturally select features for multi-class without bothering further heuristic strategy. Rather than directly solving the multi-class hinge loss with $\ell_{2,1}$-norm regularization minimization, which has not been solved before due to its optimization difficulty, we are the first to give an efficient algorithm bridging the new problem with a previous solvable optimization problem to do multi-class feature selection. A global convergence proof for our method is also presented. Via the proposed efficient algorithm, we select features across multiple classes with jointly sparsity, \emph{i.e.}, each feature has either small or large score over all classes. Comprehensive experiments have been performed on six bioinformatics data sets to show that our method can obtain better or competitive performance compared with exiting state-of-art multi-class feature selection approaches.