Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 59, Issue 3, Pages 2403-2429Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.3002724
Keywords
Go decomposition (GoDec); low-rank and sparsity-matrix decomposition (LRaSMD); minimax-singular value decomposition (MX-SVD); orthogonal subspace projection GoDec (OSP-GoDec); Reed and Xiaoli anomaly detector (RX-AD); virtual dimensionality (VD)
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Funding
- Fundamental Research Funds for Central Universities [3132019341]
- Nature Science Foundation of Liaoning Province [20180550018]
- National Nature Science Foundation of China [61601077, 61971082, 61890964]
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This article introduces a method called OSPGoDec for LRaSMD, which implements low-rank and sparse matrix decomposition through an iterative process and solves the parameter determination issue using VD and MX-SVD. Experimental results demonstrate that OSPGoDec shows more efficient performance in anomaly detection.
Low-rank and sparsity-matrix decomposition (LRaSMD) has received considerable interests lately. One of effective methods for LRaSMD is called go decomposition (GoDec), which finds low-rank and sparse matrices iteratively subject to the predetermined low-rank matrix order m and sparsity cardinality k. This article presents an orthogonal subspace-projection (OSP) version of GoDec to be called OSPGoDec, which implements GoDec in an iterative process by a sequence of OSPs to find desired low-rank and sparse matrices. In order to resolve the issues of empirically determining p = m + j and k, the well-known virtual dimensionality (VD) is used to estimate p in conjunction with the Kuybeda et al. developed minimax-singular value decomposition (MX-SVD) in the maximum orthogonal complement algorithm (MOCA) to estimate k. Consequently, LRaSMD can be realized by implementing OSP-GoDec using p and k determined by VD and MX-SVD, respectively. Its application to anomaly detection demonstrates that the proposed OSP-GoDec coupled with VD and MX-SVD performs very effectively and better than the commonly used LRaSMD-based anomaly detectors.
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