Analysis dictionary learning based on summation of blocked determinants measure of sparseness
Analysis dictionary learning based on summation of blocked determinants measure of sparseness
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DOI：
10.1109/icdsp.2015.7251864
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
Yujie Li;Shuxue Ding;Zhenni Li
中科院分区：
文献类型：
--
作者：
Yujie Li;Shuxue Ding;Zhenni Li
本文通过分析模型解决了字典学习和稀疏表示问题。尽管已经在文献中进行了研究，但仍然没有在字典学习的背景下对非负信号表示进行研究。为了测量稀疏性，在本文中，我们提出了一种称为分块行列式求和的度量。基于该方法，推导了一种新的分析稀疏模型，并提出了一种迭代稀疏最大化方法来求解该模型。在该方法中，非负稀疏表示问题可以转化为针对字典的行到行优化，然后使用二次规划（QP）技术来优化每一行。分析字典恢复的数值实验表明了该算法的有效性。
This paper addresses the dictionary learning and sparse representation with the analysis model. Though it has been studied in the literature, there is still not an investigation in the context of dictionary learning for nonnegative signal representation. For measuring the sparseness, in this paper, we propose a measure that is so called the summation of blocked determinants. Based on this measure, a new analysis sparse model is derived, and an iterative sparseness maximization approach is proposed to solve this model. In the approach, the nonnegative sparse representation problem can be cast into row-to-row optimizations with respect to the dictionary, and then the quadratic programming (QP) technique is used to optimize each row. Numerical experiments on recovery of analysis dictionary show the effectiveness of the proposed algorithm.