Smooth sparse coding via marginal regression for learning sparse representations

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• 作者：Krishnakumar Balasubramanian^a ; ^{krizna@stat.wisc.edu" class="auth_mail" title="E-mail the corresponding author} ; Kai Yu^b ; ^{yukai@baidu.com" class="auth_mail" title="E-mail the corresponding author} ; Guy Lebanon^c ; ^{glebanon@gmail.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Sparse coding ; Dictionary learning ; Vision
• 刊名：Artificial Intelligence
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：238
• 期：Complete
• 页码：83-95
• 全文大小：413 K

文摘

We propose and analyze a novel framework for learning sparse representations based on two statistical techniques: kernel smoothing and marginal regression. The proposed approach provides a flexible framework for incorporating feature similarity or temporal information present in data sets via non-parametric kernel smoothing. We provide generalization bounds for dictionary learning using smooth sparse coding and show how the sample complexity depends on the L₁$L_1$ norm of kernel function used. Furthermore, we propose using marginal regression for obtaining sparse codes which significantly improves the speed and allows one to scale to large dictionary sizes easily. We demonstrate the advantages of the proposed approach, both in terms of accuracy and speed by extensive experimentation on several real data sets. In addition, we demonstrate how the proposed approach can be used for improving semi-supervised sparse coding.