二元矩阵分解Matrix factorization with binary components |
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课程网址: | http://videolectures.net/machine_slawski_binary_components/ |
主讲教师: | Martin Slawski |
开课单位: | 萨尔大学 |
开课时间: | 2014-11-07 |
课程语种: | 英语 |
中文简介: | 受计算生物学应用的启发,我们考虑了在其中一个因子上具有{0,1}-约束的约束低秩矩阵分解问题。除了与更一般的矩阵分解方案共享的非凸性之外,我们的问题还因尺寸为2m的组合约束集而更加复杂⋅r、 其中m是数据点的维数,r是因子分解的秩。尽管存在明显的困难,我们仍提供−根据Arora等人最近关于非负矩阵分解的工作(2012)− 一种算法,在最坏的情况下,该算法在精确情况下通过O阶运算(mr2r+mnr)可证明地恢复基础因子分解。为了得到这个结果,我们引用了以组合数学中的一个基本结果为中心的理论,即Littlewood-Offord引理。 |
课程简介: | Motivated by an application in computational biology, we consider constrained low-rank matrix factorization problems with {0,1}-constraints on one of the factors. In addition to the the non-convexity shared with more general matrix factorization schemes, our problem is further complicated by a combinatorial constraint set of size 2m⋅r, where m is the dimension of the data points and r the rank of the factorization. Despite apparent intractability, we provide −in the line of recent work on non-negative matrix factorization by Arora et al.(2012)− an algorithm that provably recovers the underlying factorization in the exact case with operations of the order O(mr2r+mnr) in the worst case. To obtain that result, we invoke theory centered around a fundamental result in combinatorics, the Littlewood-Offord lemma. |
关 键 词: | 计算生物学; 矩阵分解; 因子分解 |
课程来源: | 视频讲座网 |
数据采集: | 2022-12-20:chenjy |
最后编审: | 2023-05-11:chenjy |
阅读次数: | 30 |