Results 271 to 280 of about 1,958,095 (294)
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Subspace identification for predictive state representation by nuclear norm minimization
2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2014Predictive State Representations (PSRs) are dynamical systems models that keep track of the system's state using predictions of future observations. In contrast to other models of dynamical systems, such as partially observable Markov decision processes, PSRs produces more compact models and can be consistently learned using statistics of the execution
Glaude, Hadrien +2 more
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Event Recovery by Faster Truncated Nuclear Norm Minimization
2015When we want to know an event we are concerned, it is likely that the collected information is incomplete which may severely affect the consequent analysis. In this paper, we focus on the event recovery problem that aims to discover missing historical information for a certain event based on the limited known information. We formulate an event as a two
Debing Zhang +5 more
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Rank Constrained Nuclear Norm Minimization With Application to Image Denoising
Signal Processing, 2016In the low rank matrix approximation problem, the well known nuclear norm minimization (NNM) problem plays a crucial role and attracts significant interests in recent years. In NNM the regularization parameter λ plays a decisive part, λ controls both the rank of the solution and the extent of the thresholding.
Xiangchu Feng, Weiwei Wang, Xixi Jia
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International Journal of Machine Learning and Cybernetics, 2014
Matrix completion problem refers to complete a low-rank matrix by observing only a few elements of the matrix. A traditional model to solve this problem is to transfer the minimization of rank into a nuclear norm minimization by replacing the rank function with nuclear norm.
Juan Geng, Lai-Sheng Wang, Ai-Min Fu
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Matrix completion problem refers to complete a low-rank matrix by observing only a few elements of the matrix. A traditional model to solve this problem is to transfer the minimization of rank into a nuclear norm minimization by replacing the rank function with nuclear norm.
Juan Geng, Lai-Sheng Wang, Ai-Min Fu
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Identification of power spectra by reweighted and regularized nuclear norm minimization
2015 10th Asian Control Conference (ASCC), 2015In this paper, we study model order choice in subspace identification algorithms using uniformly spaced spectrum measurements. In these algorithms, model order is determined by singular-value decomposition of a structured matrix constructed from spectrum measurements.
Hüseyin Akçay, Semiha Türkay
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Causal dynamic MRI reconstruction via nuclear norm minimization
Magnetic Resonance Imaging, 2012This work addresses the problem of online reconstruction of dynamic magnetic resonance images (MRI). The proposed method reconstructs the difference between the images of previous and current time frames. This difference image is modeled as a rank deficient matrix and is solved from the partially sampled k-space data via nuclear norm minimization.
Angshul, Majumdar, Rabab K, Ward
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Localization of Wireless Sensors via Nuclear Norm for Rank Minimization
2010 IEEE Global Telecommunications Conference GLOBECOM 2010, 2010The low rank feature of location estimation in Wireless Sensor Networks (WSNs) makes it feasible to use nuclear norm minimization as an accurate and fast solution for low-dimensional embedding problems. In this paper, a novel localization algorithm for WSNs is proposed by using nuclear norm for rank minimization.
Chen Feng 0001 +3 more
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Low-Rank Matrix Recovery via Regularized Nuclear Norm Minimization
Applied and Computational Harmonic Analysis, 2021Jianjun Wang, Wendong Wang
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Sobel Edge Detection Based on Weighted Nuclear Norm Minimization Image Denoising
Electronics, 2021Guiling Sun, Xiaochao Liu, Bowen Zheng
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Image Restoration Using Probability-Inducing Nuclear Norm Minimization
2022 IEEE International Conference on Image Processing (ICIP), 2022Zhongxing Zhang 0001 +2 more
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