资源分类
》 资源 》论文》Local Low-Rank Matrix Approximation

Local Low-Rank Matrix Approximation

2020-03-02 | |  57 |   50 |   0

Abstract

Matrix approximation is a common tool in recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix is locally of low-rank, leading to a representation of the observed matrix as a weighted sum of low-rank matrices. We analyze the accuracy of the proposed local lowrank modeling. Our experiments show improvements in prediction accuracy over classical approaches for recommendation tasks.

上一篇：A Local Algorithm for Finding Well-Connected Clusters

下一篇：Average Reward Optimization Objective In Partially Observable Domains

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

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

  • Rating-Boosted La...

    The performance of a recommendation system reli...