论文标题
终身矩阵完成稀疏数量
Lifelong Matrix Completion with Sparsity-Number
论文作者
论文摘要
先前在各种自适应和被动设置下研究了矩阵完成问题。以前，研究人员已使用相干参数提出了被动，两相和单相算法，以及使用Sparsity-number的多相算法。已经表明，在许多情况下，使用稀疏数量的方法达到了理论下限。但是，上述方法通过矩阵完成过程在许多阶段运行，因此在每个阶段都可以更有用的决定。因此，该方法比以前的算法自然。在本文中，我们使用了稀疏数量的想法，并提出和单相列空间恢复算法，该算法可以扩展到两相精确的矩阵完成算法。此外，我们表明这些方法与多相基质恢复算法一样有效。我们提供实验证据来说明算法的性能。
Matrix completion problem has been previously studied under various adaptive and passive settings. Previously, researchers have proposed passive, two-phase and single-phase algorithms using coherence parameter, and multi phase algorithm using sparsity-number. It has been shown that the method using sparsity-number reaching to theoretical lower bounds in many conditions. However, the aforementioned method is running in many phases through the matrix completion process, therefore it makes much more informative decision at each stage. Hence, it is natural that the method outperforms previous algorithms. In this paper, we are using the idea of sparsity-number and propose and single-phase column space recovery algorithm which can be extended to two-phase exact matrix completion algorithm. Moreover, we show that these methods are as efficient as multi-phase matrix recovery algorithm. We provide experimental evidence to illustrate the performance of our algorithm.
