Fast greedy algorithms for dictionary selection with generalized sparsity constraints
Fast greedy algorithms for dictionary selection with generalized sparsity constraints
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发表时间：
2018-09
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通讯作者：
K. Fujii;Tasuku Soma
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作者：
K. Fujii;Tasuku Soma
在字典选择中，从有限的候选中选择几个原子，这些原子成功地近似稀疏表示中的给定数据点。我们提出了一种新颖的高效贪心算法用于字典选择。我们的算法不仅比已知方法运行速度快得多，而且还可以处理更复杂的稀疏性约束，例如平均稀疏性。通过数值实验，我们表明我们的算法优于已知的字典选择方法，在更短的运行时间内实现了与字典学习算法竞争的性能。
In dictionary selection, several atoms are selected from finite candidates that successfully approximate given data points in the sparse representation. We propose a novel efficient greedy algorithm for dictionary selection. Not only does our algorithm work much faster than the known methods, but it can also handle more complex sparsity constraints, such as average sparsity. Using numerical experiments, we show that our algorithm outperforms the known methods for dictionary selection, achieving competitive performances with dictionary learning algorithms in a smaller running time.