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l0-norm based structural sparse least square regression for feature selection
Han, Jiuqi; Sun, Zhengya; Hao, Hongwei
Source PublicationPattern Recognition
2015-06
Volume48Issue:12Pages:3927 - 3940
AbstractThis 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.
KeywordStructural 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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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11659
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Affiliation中国科学院自动化研究所
Recommended Citation
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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