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Reweighted Nuclear Norm Minimization With Application to System Identification [PDF]
Proceedings of the 2010 American Control Conference, 2010 The matrix rank minimization problem consists of finding a matrix of minimum rank that satisfies given convex constraints. It is NP-hard in general and has applications in control, system identification, and machine learning. Reweighted trace minimization has been considered as an iterative heuristic for this problem.
Maryam Fazel
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Truncated nuclear norm minimization for tensor completion
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014In this paper, a tensor n-mode matrix unfolding truncated nuclear norm is proposed, which is extended from the matrix truncated nuclear norm, to tensor completion problem. The alternating direction method of multipliers is utilized to solve this optimization problem.
Longting Huang +3 more
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Principal Component Analysis based on Nuclear norm Minimization
Neural Networks, 2019Principal component analysis (PCA) is a widely used tool for dimensionality reduction and feature extraction in the field of computer vision. Traditional PCA is sensitive to outliers which are common in empirical applications. Therefore, in recent years, massive efforts have been made to improve the robustness of PCA.
Jian-Xun Mi +5 more
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Background subtraction via truncated nuclear norm minimization
2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2017The background subtraction is one of the main topics in video analysis. Among the various conventional approaches related to this topic, low-rank and sparse decomposition based method has shown a great ability to decompose foreground and background. This method approximates the matrix rank by robust principal component analysis via the nuclear norm ...
Hyeonggwon Kim, Yoonsik Choe
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Nuclear norm minimization for blind subspace identification (N2BSID)
2015 54th IEEE Conference on Decision and Control (CDC), 2015In many practical applications of system identification, it is not feasible to measure both the inputs applied to the system as well as the output. In such situations, it is desirable to estimate both the inputs and the dynamics of the system simultaneously; this is known as the blind identification problem.
Dexter Scobee +5 more
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Nuclear norm minimization and tensor completion in exploration seismology
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013We consider the problem of multidimensional seismic data signal recovery and noise attenuation. These data are multi-dimensional signals that can be described via a low-rank fourth-order tensor in the frequency-space domain. Tensor completion strategies can be used to recover unrecorded observations and to improve the signal-to-noise ratio of seismic ...
Nadia Kreimer, Mauricio D. Sacchi
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Regularization Paths for Re-Weighted Nuclear Norm Minimization
IEEE Signal Processing Letters, 2015We consider a class of weighted nuclear norm optimization problems with important applications in signal processing, system identification, and model order reduction. The nuclear norm is commonly used as a convex heuristic for matrix rank constraints.
Niklas Blomberg +2 more
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Hammerstein system identification using nuclear norm minimization
Automatica, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Younghee Han, Raymond A. de Callafon
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Ultrasound Image Restoration Using Weighted Nuclear Norm Minimization
2020 25th International Conference on Pattern Recognition (ICPR), 2021Ultrasound images are often contaminated by speckle noise during the acquisition process, which influences the performance of subsequent applications. The paper introduces a nonconvex low-rank matrix approximation model for ultrasound images restoration, which integrates the weighted nuclear norm minimization (WNNM) and data fidelity term.
Hanmei Yang +4 more
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Tight recovery thresholds and robustness analysis for nuclear norm minimization
2011 IEEE International Symposium on Information Theory Proceedings, 2011Nuclear norm minimization (NNM) has recently gained significant attention for its use in rank minimization problems. Using null space characterizations, recovery thresholds for NNM have been previously studied for the case of Gaussian measurements as matrix dimensions tend to infinity.
Oymak, Samet, Hassibi, Babak
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