Online completion of Ill-conditioned low-rank matrices
Online completion of Ill-conditioned low-rank matrices
复制标题
病态低秩矩阵的在线完成
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
L. Balzano
中科院分区：
文献类型：
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作者：
Ryan Kennedy;C. J. Taylor;L. Balzano
研究了病态低秩矩阵的在线完备化问题。虽然最近提出了许多矩阵完备化算法，但它们经常与病态矩阵作斗争，并且需要很长时间才能收敛。在本文中，我们提出了一种新的算法称为极性增量矩阵完成（PIMC）来解决这个问题。我们的方法是基于GROUSE算法，我们展示了如何极分解可以用来保持奇异值矩阵的估计，以更好地处理病态问题。该方法也是在线的，允许它应用于流数据。我们评估我们的算法的合成数据和一个真实的“结构从运动”的计算机视觉社区的数据集，并表明，PIMC优于类似的方法。
We consider the problem of online completion of ill-conditioned low-rank matrices. While many matrix completion algorithms have been proposed recently, they often struggle with ill-conditioned matrices and take a long time to converge. In this paper, we present a new algorithm called Polar Incremental Matrix Completion (PIMC) to address this problem. Our method is based on the GROUSE algorithm, and we show how a polar decomposition can be used to maintain an estimate of the singular value matrix to better deal with ill-conditioned problems. The method is also online, allowing it to be applied to streaming data. We evaluate our algorithm on both synthetic data and a real "structure from motion" dataset from the computer vision community, and show that PIMC outperforms similar methods.