Sampling approach to sparse approximation problem: determining degrees of freedom by simulated annealing
Sampling approach to sparse approximation problem: determining degrees of freedom by simulated annealing
复制标题
稀疏逼近问题的采样方法：通过模拟退火确定自由度
DOI：
10.1109/eusipco.2016.7760448
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Yoshiyuki Kabashima.
中科院分区：
文献类型：
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作者：
Tomoyuki Obuchi;Yoshiyuki Kabashima.
从一个固定的矩阵中选择列向量的一个小的组合来近似一个高维向量，这在几个不同的学科中都有积极的争论。在本文中，抽样方法的基础上的蒙特卡罗方法提出了一个有效的解决方案，这样的问题。特别是，使用模拟退火（SA），元启发式优化算法，确定自由度（使用的列数）交叉验证的重点和测试。合成模型上的测试表明，我们基于SA的方法可以找到近似问题的近最优解，当与CV框架相结合时，它可以优化泛化能力。它的效用也证实了应用程序的一个真实世界的超新星数据集。
The approximation of a high-dimensional vector by a small combination of column vectors selected from a fixed matrix has been actively debated in several different disciplines. In this paper, a sampling approach based on the Monte Carlo method is presented as an efficient solver for such problems. Especially, the use of simulated annealing (SA), a metaheuristic optimization algorithm, for determining degrees of freedom (the number of used columns) by cross validation is focused on and tested. Test on a synthetic model indicates that our SA-based approach can find a nearly optimal solution for the approximation problem and, when combined with the CV framework, it can optimize the generalization ability. Its utility is also confirmed by application to a real-world supernova data set.