4.7 Article

Direct-Optimization-Based DC Dictionary Learning With the MCP Regularizer

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3114400

关键词

Machine learning; Optimization; Dictionaries; Convergence; Convex functions; Signal processing algorithms; Approximation algorithms; Convergence analysis; dictionary learning; direct optimization; minimax concave penalty (MCP) regularizer

向作者/读者索取更多资源

In this article, a novel direct-optimization-based dictionary learning algorithm is proposed using the minimax concave penalty as a sparsity regularizer. The algorithm can enforce strong sparsity and obtain accurate estimation. The nonconvex MCP is decomposed into two convex components and processed using convex functions algorithm and nonconvex proximal-splitting algorithm. The proposed algorithm can be applied to a broader class of dictionary learning problems and has proven convergence guarantee.
Direct-optimization-based dictionary learning has attracted increasing attention for improving computational efficiency. However, the existing direct optimization scheme can only be applied to limited dictionary learning problems, and it remains an open problem to prove that the whole sequence obtained by the algorithm converges to a critical point of the objective function. In this article, we propose a novel direct-optimization-based dictionary learning algorithm using the minimax concave penalty (MCP) as a sparsity regularizer that can enforce strong sparsity and obtain accurate estimation. For solving the corresponding optimization problem, we first decompose the nonconvex MCP into two convex components. Then, we employ the difference of the convex functions algorithm and the nonconvex proximal-splitting algorithm to process the resulting subproblems. Thus, the direct optimization approach can be extended to a broader class of dictionary learning problems, even if the sparsity regularizer is nonconvex. In addition, the convergence guarantee for the proposed algorithm can be theoretically proven. Our numerical simulations demonstrate that the proposed algorithm has good convergence performances in different cases and robust dictionary-recovery capabilities. When applied to sparse approximations, the proposed approach can obtain sparser and less error estimation than the different sparsity regularizers in existing methods. In addition, the proposed algorithm has robustness in image denoising and key-frame extraction.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据