非负矩阵分解中贝叶斯泛化误差的上界

Dec, 2016

Upper Bound of Bayesian Generalization Error in Non-Negative Matrix Factorization

Naoki Hayashi, Sumio Watanabe

TL;DR本文研究了非负矩阵分解的真实对数规范阈值，并在贝叶斯学习中给出了一种上界估计，结果表明如果应用贝叶斯学习，则可以使矩阵分解的泛化误差小于常规统计模型。

Abstract

non-negative matrix factorization (NMF) is a new knowledge discovery method that is used for text mining, signal processing, bioinformatics