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

Zhenni Li

doi:10.1109/TNNLS.2021.3114400

2023-07-01

PRE

Zhenni Li

Zuyuan Yang

赵昊立 (Zhao, Haoli)

Xie Shengli (Shengli Xie) *

DOI：10.1109/TNNLS.2021.3114400

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摘要

En 中文

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.

Keyword:

Machine learning

Optimization

Dictionaries

Convergence

Convex functions

Signal processing algorithms

Approximation algorithms

Convergence analysis

dictionary learning

direct optimization

minimax concave penalty (MCP) regularizer