图 书 馆
用线性程序分解非负矩阵Factoring nonnegative matrices with linear programs |
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| 课程网址: | http://videolectures.net/machine_bittorf_nonnegative_matrices/ |
| 主讲教师: | Victor Bittorf |
| 开课单位: | 威斯康星大学 |
| 开课时间: | 2013-01-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This paper describes a new approach for computing nonnegative matrix factorizations (NMFs) with linear programming. The key idea is a data-driven model for the factorization, in which the most salient features in the data are used to express the remaining features. More precisely, given a data matrix X, the algorithm identifies a matrix C that satisfies X = CX and some linear constraints. The matrix C selects features, which are then used to compute a low-rank NMF of X. A theoretical analysis demonstrates that this approach has the same type of guarantees as the recent NMF algorithm of Arora et al.~(2012). In contrast with this earlier work, the proposed method has (1) better noise tolerance, (2) extends to more general noise models, and (3) leads to efficient, scalable algorithms. Experiments with synthetic and real datasets provide evidence that the new approach is also superior in practice. An optimized C++ implementation of the new algorithm can factor a multi-Gigabyte matrix in a matter of minutes. |
| 关 键 词: | 线性规划; 非负矩阵因子分解; 数据驱动模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-14：yumf |
| 阅读次数: | 46 |