图 书 馆
矩阵分解模型的统一视图A Unified View of Matrix Factorization Models |
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| 课程网址: | http://videolectures.net/ecmlpkdd08_singh_auvo/ |
| 主讲教师: | Ajit Singh; Geoffrey J. Gordon |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2008-10-10 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present a unified view of matrix factorization that frames the differences among popular methods, such as NMF, Weighted SVD, E-PCA, MMMF, pLSI, pLSI-pHITS, Bregman co-clustering, and many others, in terms of a small number of modeling choices. Many of these approaches can be viewed as minimizing a generalized Bregman divergence, and we show that (i) a straightforward alternating projection algorithm can be applied to almost any model in our unified view; (ii) the Hessian for each projection has special structure that makes a Newton projection feasible, even when there are equality constraints on the factors, which allows for matrix co-clustering; and (iii) alternating projections can be generalized to simultaneously factor a set of matrices that share dimensions. These observations immediately yield new optimization algorithms for the above factorization methods, and suggest novel generalizations of these methods such as incorporating row/column biases, and adding or relaxing clustering constraints. |
| 关 键 词: | 计算机科学; 机器学习; 矩阵 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-22：chenxin |
| 阅读次数: | 37 |