图 书 馆
集体矩阵分解的关系学习Relational Learning as Collective Matrix Factorization |
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| 课程网址: | http://videolectures.net/cmulls08_singh_rlm/ |
| 主讲教师: | Ajit Singh |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2008-02-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present a unified view of matrix factorization models, including singular value decompositions, non-negative matrix factorization, probabilistic latent semantic indexing, and generalizations of these models to exponential families and non-regular Bregman divergences. One can model relational data as a set of matrices, where each matrix represents the value of a relation between two entity-types. Instead of a single matrix, relational data is represented as a set of matrices with shared dimensions and tied low-rank representation. Our example domain is augmented collaborative filtering, where both user ratings and side information about items are available. To predict the value of a relation, we extend Bregman matrix factorization to a set of related matrices. Using an alternating minimization scheme, we show the existence of a practical Newton step. The use of stochastic second-order methods for large matrices is also covered. |
| 关 键 词: | 机械学习; 矩阵分解模型; 数学建模 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-22：chenxin |
| 阅读次数: | 79 |