Sparse representation and dictionary learning based on alternating parallel coordinate descent
Sparse representation and dictionary learning based on alternating parallel coordinate descent
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DOI：
10.1109/icawst.2013.6765490
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
Zunyi Tang;Toshiyo Tamura;Shuxue Ding;Zhenni Li
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文献类型：
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作者：
Zunyi Tang;Toshiyo Tamura;Shuxue Ding;Zhenni Li
最近，稀疏表示通过一个过完备的字典已成为一个主要的研究领域，在信号处理。许多努力都集中在字典学习算法的发展，使信号的稀疏表示可以有效地执行。在本文中，我们提出了一种方法来学习信号相关的过完备字典。这是通过将信号的稀疏表示作为具有稀疏约束的矩阵分解问题来实现的。通过推广传统的坐标下降法，我们开发了一个所谓的稀疏交替并行坐标下降（SAPCD）算法，它是通过迭代求解两个最优问题，字典的学习过程和用于构建信号的系数的估计过程。数值实验表明，该算法的性能优于著名的K-SVD算法和其他几种算法的比较。
Recently, sparse representations via an overcomplete dictionary has become a major field of research in signal processing. Much efforts have been focused on the development of dictionary learning algorithms so that the sparse representation of signals can be efficiently performed. In this paper, we propose a method for learning a signal dependent overcomplete dictionary. This is accomplished by posing the sparse representation of signals as a problem of matrix factorization with a sparsity constraint. By generalizing the conventional coordinate descent method, we develop a so-called sparse alternating parallel coordinate descent (SAPCD) algorithm, which is structured by iteratively solving the two optimal problems, the learning process of the dictionary and the estimating process of the coefficients for constructing the signals. Numerical experiments demonstrate that the proposed algorithm performs better than the famous K-SVD algorithm and several other algorithms for comparison.