Robust PCA With Partial Subspace Knowledge
نویسندگان
چکیده
منابع مشابه
Robust PCA and Robust Subspace Tracking
Principal Components Analysis (PCA) is one of the most widely used dimension reduction techniques. Given a matrix of clean data, PCA is easily accomplished via singular value decomposition (SVD) on the data matrix. While PCA for relatively clean data is an easy and solved problem, it becomes much harder if the data is corrupted by even a few outliers. The reason is that SVD is sensitive to outl...
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Dynamic robust PCA refers to the dynamic (time-varying) extension of the robust PCA (RPCA) problem. It assumes that the true (uncorrupted) data lies in a low-dimensional subspace that can change with time, albeit slowly. The goal is to track this changing subspace over time in the presence of sparse outliers. This work provides the first guarantee for dynamic RPCA that holds under weakened vers...
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In this work, we study the robust subspace tracking (RST) problem and obtain one of the first two provable guarantees for it. The goal of RST is to track sequentially arriving data vectors that lie in a slowly changing low-dimensional subspace, while being robust to corruption by additive sparse outliers. It can also be interpreted as a dynamic (time-varying) extension of robust PCA (RPCA), wit...
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This paper presents GRASTA (Grassmannian Robust Adaptive Subspace Tracking Algorithm), an efficient and robust online algorithm for tracking subspaces from highly incomplete information. The algorithm uses a robust l-norm cost function in order to estimate and track non-stationary subspaces when the streaming data vectors are corrupted with outliers. We apply GRASTA to the problems of robust ma...
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The robust principal component analysis (RPCA) problem seeks to separate lowrank trends from sparse outliers within a data matrix, that is, to approximate a n⇥d matrix D as the sum of a low-rank matrix L and a sparse matrix S. We examine the robust principal component analysis (RPCA) problem under data compression, where the data Y is approximately given by (L+S)·C, that is, a low-rank + sparse...
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2015
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2015.2421485