排名2 代最小平方:从少数条目中有效地恢复III条件低排行矩阵
Jonathan Bauch1, Boaz Nadler1, Pini Zilber1
1Faculty of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot, 76100 Israel.
概括
一种新的代方法,Rank-2R 代最小方程 (R2RILS),可以有效地完成低等级矩阵. 这种计算成本低廉的算法可以从最小的数据中恢复不良条件的矩阵,即使有噪声.
科学领域:
- 数字分析 数字分析
- 线性代数 线性代数
- 数据科学数据科学数据科学
背景情况:
- 矩阵完成对于重建不完整数据集至关重要.
- 现有的代方法经常面临计算或记忆方面的挑战.
- 分因子类型的算法很常见,但有局限性.
研究的目的:
- 介绍一种新的,计算效率高的代方法来完成低级矩阵.
- 解决现有的因数分解类型算法的局限性.
- 开发一种具有较低内存要求和计算成本的方法.
主要方法:
- 提出一个新的代算法,Rank-2R 代最小方程 (R2RILS).
- 在临时估计中采用过度参数化的排名2r结构,而不是在排名r的多元组上进行优化.
- 在每次代中解决计算便宜的稀疏最小平方问题.
主要成果:
- R2RILS展示了低内存要求和计算效率.
- 该算法从理论上分析了排名1矩阵的情况.
- 在经验上,R2RILS从接近信息极限的观测中恢复了不良条件的低等级矩阵.
- 该方法在添加噪声的情况下显示稳定性.
结论:
- R2RILS为低级别的矩阵完成提供了一个简单,高效和强大的解决方案.
- 该算法即使在有限的观测和杂的数据中也表现良好.
- 这种方法推进了矩阵完成领域的实用优势.
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