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A Measurement-Efficient Low-Rank Matrix Recovery Approach
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018This paper presents a novel low-rank matrix recovery approach that jointly performs measurement collection and matrix estimation to improve the overall sample efficiency under unknown rank information. It builds on a key observation that the minimum number of measurements needed for matrix rank estimation can be much less than that for matrix recovery.
Yanbo Wang, Zhi Tian, Yue Wang 0019
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Low-rank matrix recovery of dynamic events
2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2017Low-rank matrix recovery problems arise in a variety of scientific and engineering applications. For instance, blind de-convolution in signal processing and communication, phase retrieval in computational imaging, and recommendation systems in machine learning.
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Double Low Rank Matrix Recovery for Saliency Fusion
IEEE Transactions on Image Processing, 2016In this paper, we address the problem of fusing various saliency detection methods such that the fusion result outperforms each of the individual methods. We observe that the saliency regions shown in different saliency maps are with high probability covering parts of the salient object.
Junxia Li +4 more
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Sparse array imaging using low-rank matrix recovery
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017Co-array based processing enables sparse arrays to achieve the resolution of uniform arrays in array imaging applications. In particular, a desired point spread function may be synthesized by coherently adding together several component images obtained using different complex-valued physical element weights.
Koivunen, Visa, Rajamäki, Robin
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Low-Rank Matrix Recovery Via Robust Outlier Estimation
IEEE Transactions on Image Processing, 2018In practice, high-dimensional data are typically sampled from low-dimensional subspaces, but with intrusion of outliers and/or noises. Recovering the underlying structure and the pollution from the observations is of utmost importance to understanding the data.
Xiaojie Guo 0001, Zhouchen Lin
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Accelerated algorithms for low-rank matrix recovery
SPIE Proceedings, 2013In recent years, Low-rank matrix recovery from corrupted noise matrix has attracted interests as a very effective method in high-dimensional data. And its fast algorithm has become a research focus. This paper we first review the basic theory and typical accelerated algorithms.
Shuiping Zhang, Jinwen Tian
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Low-Rank Matrix Recovery via Continuation-Based Approximate Low-Rank Minimization
2018Low-rank matrix recovery (LRMR) is to recover the underlying low-rank structure from the degraded observations, and has myriad formulations, for example robust principal component analysis (RPCA). As a core component of LRMR, the low-rank approximation model attempts to capture the low-rank structure by approximating the \(\ell _0\)-norm of all the ...
Xiang Zhang 0008 +5 more
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A Sparse and Low-Rank Matrix Recovery Model for Saliency Detection
2018The previous low-rank matrix recovery model for saliency detection have a large of problem that the transform matrix obtained on the open datasets may not be suitable for the detecting image and the transform matrix fails to combine the low-level features of the image.
Chao Wang +3 more
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Color Image Recovery Using Low-Rank Quaternion Matrix Completion Algorithm
IEEE Transactions on Image Processing, 2022Jifei Miao, Kit Ian Kou
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Sharp RIP bound for sparse signal and low-rank matrix recovery
Applied and Computational Harmonic Analysis, 2013Tony Cai, Anru R Zhang
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