资源分类
》 资源 》论文》Implicit Regularization in Variational Bayesian Matrix Factorization

Implicit Regularization in Variational Bayesian Matrix Factorization

2020-02-26 | |  66 |   50 |   0

Abstract

Matrix factorization into the product of lowrank matrices induces non-identi?ability, i.e., the mapping between the target matrix and factorized matrices is not one-to-one. In this paper, we theoretically investigate the in?uence of non-identi?ability on Bayesian matrix factorization. More speci?cally, we show that a variational Bayesian method involves regularization e?ect even when the prior is non-informative, which is intrinsically different from the maximum a posteriori approach. We also extend our analysis to empirical Bayes scenarios where hyperparameters are also learned from data.

上一篇：Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda

下一篇：Making Large-Scale Nystrom Approximation Possible

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...