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Adaptive Low-Rank Matrix Completion.
- Source :
- IEEE Transactions on Signal Processing; Jul2017, Vol. 65 Issue 14, p3603-3616, 14p
- Publication Year :
- 2017
-
Abstract
- The low-rank matrix completion problem is fundamental to a number of tasks in data mining, machine learning, and signal processing. This paper considers the problem of adaptive matrix completion in time-varying scenarios. Given a sequence of incomplete and noise-corrupted matrices, the goal is to recover and track the underlying low rank matrices. Motivated from the classical least-mean square (LMS) algorithms for adaptive filtering, three LMS-like algorithms are proposed for estimating and tracking low-rank matrices. Performance of the proposed algorithms is provided in form of nonasymptotic bounds on the tracking mean-square error. Tracking performance of the algorithms is also studied via detailed simulations over real-world datasets. [ABSTRACT FROM PUBLISHER]
- Subjects :
- DATA mining
SIGNAL processing
LEAST squares
ADAPTIVE control systems
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 1053587X
- Volume :
- 65
- Issue :
- 14
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 124146178
- Full Text :
- https://doi.org/10.1109/TSP.2017.2695450