核心思想
传统的
Local的
Local组合成最终的model
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Local low-rank matrix approximation | |||||||||||
| 文献作者 | Joonseok Lee; Seungyeon Kim; Guy Lebanon | ||||||||||
| 文献发表年限 | 2013 | ||||||||||
| 文献关键字 | |||||||||||
| 摘要描述 | |||||||||||
| 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 low-rank modeling. Our experiments show improvements in prediction accuracy over classical approaches for recommendation tasks. | |||||||||||
