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Article overview
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Learning Deep $ell_0$ Encoders | | Zhangyang Wang
; Qing Ling
; Thomas S. Huang
; | | Date: |
1 Sep 2015 | | Abstract: | Despite its nonconvex, intractable nature, $ell_0$ sparse approximation is
desirable in many theoretical and application cases. We study the $ell_0$
sparse approximation problem with the tool of deep learning, by proposing Deep
$ell_0$ Encoders. Two typical forms, the $ell_0$ regularized problem and the
$M$-sparse problem, are investigated. Based on solid iterative algorithms, we
model them as feed-forward neural networks, through introducing novel neurons
and pooling functions. The deep encoders enjoy faster inference, larger
learning capacity, and better scalability compared to conventional sparse
coding solutions. Furthermore, when applying them to classification and
clustering, the models can be conveniently optimized from end to end, using
task-driven losses. Numerical results demonstrate the impressive performances
of the proposed encoders. | | Source: | arXiv, 1509.0153 | | Services: | Forum | Review | PDF | Favorites | |
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