Empirical Bayes matrix completion
Copyright policy )Empirical Bayes matrix completion
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 (1972). Since the EB algorithm is essentially the EM algorithm applied to a simple model, it does not require heuristic parameter tuning other than tolerance. Numerical results demonstrated that the EB algorithm achieves a good trade-off between accuracy and efficiency compared to existing algorithms and that it works particularly well when the difference between the number of rows and columns is large. Application to real data also shows the practical utility of the EB algorithm.
15 pages
- University of Tokyo Japan
- RIKEN Brain Science Institute
- RIKEN Brain Science Institute Japan
- RIKEN Japan
- RIKEN Center for Brain Science Japan
singular value shrinkage, FOS: Computer and information sciences, expectation-maximization algorithm, Mathematics - Statistics Theory, Machine Learning (stat.ML), Matrix completion problems, Statistics Theory (math.ST), Methodology (stat.ME), Statistics - Machine Learning, FOS: Mathematics, Computational methods for problems pertaining to statistics, matrix completion, empirical Bayes, Statistics - Methodology
singular value shrinkage, FOS: Computer and information sciences, expectation-maximization algorithm, Mathematics - Statistics Theory, Machine Learning (stat.ML), Matrix completion problems, Statistics Theory (math.ST), Methodology (stat.ME), Statistics - Machine Learning, FOS: Mathematics, Computational methods for problems pertaining to statistics, matrix completion, empirical Bayes, Statistics - Methodology
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