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Robust feature selection via simultaneous sapped norm and sparse regularizer minimization
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DOI：10.1016/j.neucom.2017.12.055.png)
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Abstract
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High dimension is one of the key characters of big data. Feature selection, as a framework to identify a small subset of illustrative and discriminative features, has been proved as a basic solution in dealing with high-dimensional data. In previous literatures, l(2,p)-norm regularization was studied by many researches as an effective approach to select features across data sets with sparsity. However,l(2,p)-norm loss function is just robust to noise but not considering the influence of outliers. In this paper, we propose a new robust and efficient feature selection method with emphasizing Simultaneous Capped l(2)-norm loss and l(2,p)-norm regularizer Minimization (SCM). The capped l(2)-norm based loss function can effectively eliminate the influence of noise and outliers in regression and the l(2,p)-norm regularization is used to select features across data sets with joint sparsity. An efficient approach is then introduced with proved convergence. Extensive experimental studies on synthetic and real-world datasets demonstrate the effectiveness of our method in comparison with other popular feature selection methods. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Feature selection
Capped l(2)-norm loss
l(2,p)-norm regularization
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