论文标题
矩阵完成算法的调查
Survey of Matrix Completion Algorithms
论文作者
论文摘要
自Netflix宣布Netflix奖品问题以来，已经在许多不同的条件下研究了矩阵完成问题。一旦发现许多现实生活数据集可以通过低级别矩阵估算，就已经在该领域进行了许多研究工作。从那以后，压缩感应，自适应信号检测引起了许多研究人员的注意。在本调查文件中，我们将访问一些矩阵完成方法，主要是朝着被动和自适应方向的方向访问。首先，我们讨论具有凸优化的被动矩阵完成方法，以及具有自适应信号检测方法的第二个活动矩阵完成技术。传统上，许多机器学习问题在被动环境中得到解决。但是，后来观察到，自适应感测算法比以前的算法更有效地发挥作用。因此，在这种情况下已经对算法进行了广泛的研究。因此，我们将介绍本文中一些最新的自适应矩阵完成算法，同时提供被动方法。
Matrix completion problem has been investigated under many different conditions since Netflix announced the Netflix Prize problem. Many research work has been done in the field once it has been discovered that many real life dataset could be estimated with a low-rank matrix. Since then compressed sensing, adaptive signal detection has gained the attention of many researchers. In this survey paper we are going to visit some of the matrix completion methods, mainly in the direction of passive and adaptive directions. First, we discuss passive matrix completion methods with convex optimization, and the second active matrix completion techniques with adaptive signal detection methods. Traditionally many machine learning problems are solved in passive environment. However, later it has been observed that adaptive sensing algorithms many times performs more efficiently than former algorithms. Hence algorithms in this setting has been extensively studied. Therefore, we are going to present some of the latest adaptive matrix completion algorithms in this paper meanwhile providing passive methods.
