The letter deals with the problem known as robust principal component analysis (RPCA), that is, the decomposition of a data matrix as the sum of a low-rank matrix component and a sparse matrix component. After expressing the low-rank matrix component in factorized form, we develop a novel online RPCA algorithm that is based entirely on reweighted least squares recursions and is appropriate for sequential data processing. The proposed algorithm is fast, memory optimal and, as corroborated by indicative empirical results on simulated data and a video processing application, competitive to the state-of-the-art in terms of estimation performance.

中文翻译：

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在线重加权最小二乘鲁棒 PCA

这封信处理称为稳健主成分分析 (RPCA) 的问题，即将数据矩阵分解为低秩矩阵成分和稀疏矩阵成分的总和。在以分解形式表达低秩矩阵分量后，我们开发了一种新颖的在线 RPCA 算法，该算法完全基于重新加权的最小二乘递归，适用于顺序数据处理。所提出的算法速度快，内存优化，并且正如模拟数据和视频处理应用程序的指示性经验结果所证实的那样，在估计性能方面与最先进的算法竞争。