l0-norm based structural sparse least square regression for feature selection
Han, Jiuqi; Sun, Zhengya; Hao, Hongwei
2015-06
发表期刊Pattern Recognition
卷号48期号:12页码:3927 - 3940
摘要This paper presents a novel approach for feature selection with regards to the problem of structural sparse least square regression (SSLSR). Rather than employing the $l_1$-norm regularization to control the sparsity, we directly work with sparse solutions via $l_0$-norm regularization. In particular, we develop an effective greedy algorithm, where the forward and backward steps are combined adaptively, to resolve the SSLSR problem with the intractable $l_{r,0}$-norm. On the one hand, features with the strongest correlation to classes are selected in the forward steps. On the other hand, redundant features which contribute little to the improvement of the objective function are removed in the backward steps. Furthermore, we provide solid theoretical analysis to prove the effectiveness of the proposed method. Experimental results on synthetic and real world data sets from different domains also demonstrate the superiority of the proposed method over the state-of-the-arts.
关键词Structural Sparse Learning L0-norm Least Square Regression Feature Selection Adaptive Greedy Algorithm
DOIhttp://dx.doi.org/10.1016/j.patcog.2015.06.003
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文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/11659
专题数字内容技术与服务研究中心_听觉模型与认知计算
作者单位中国科学院自动化研究所
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GB/T 7714
Han, Jiuqi,Sun, Zhengya,Hao, Hongwei. l0-norm based structural sparse least square regression for feature selection[J]. Pattern Recognition,2015,48(12):3927 - 3940.
APA Han, Jiuqi,Sun, Zhengya,&Hao, Hongwei.(2015).l0-norm based structural sparse least square regression for feature selection.Pattern Recognition,48(12),3927 - 3940.
MLA Han, Jiuqi,et al."l0-norm based structural sparse least square regression for feature selection".Pattern Recognition 48.12(2015):3927 - 3940.
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