Empirical Bayes matrix completion
Empirical Bayes matrix completion
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DOI：
10.1016/j.csda.2019.02.006
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
T. Matsuda;F. Komaki
中科院分区：
文献类型：
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作者：
T. Matsuda;F. Komaki
我们开发了一个经验贝叶斯（EB）算法的矩阵完成问题。EB算法是从Efron和Morris提出的矩阵均值奇异值收缩估计出发的。由于EB算法是作为应用于简单模型的期望最大化算法推导出来的，因此除了公差之外，它不需要启发式参数调整。此外，它可以解释的观测噪声方差的异质性。数值结果表明，EB算法达到至少相当的精度，现有的算法矩阵不接近广场，它的工作特别好，当排名相对较大或观察到的条目的比例是小的。对真实的数据的应用也表明了EB算法的实用性。
We develop an empirical Bayes (EB) algorithm for the matrix completion problems. The EB algorithm is motivated from the singular value shrinkage estimator for matrix means by Efron and Morris. Since the EB algorithm is derived as the Expectation–Maximization algorithm applied to a simple model, it does not require heuristic parameter tuning other than tolerance. Also, it can account for the heterogeneity in variance of observation noise. Numerical results demonstrate that the EB algorithm attains at least comparable accuracy to existing algorithms for matrices not close to square and that it works particularly well when the rank is relatively large or the proportion of observed entries is small. Application to real data also shows the practical utility of the EB algorithm.