Results 21 to 30 of about 1,797 (255)
Sensors, 2022 Although wireless sensor networks (WSNs) have been widely used, the existence of data loss and corruption caused by poor network conditions, sensor bandwidth, and node failure during transmission greatly affects the credibility of monitoring data.
Jingfei He +3 more
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Application of Weighted Truncated p Norm in Motion Target Detection [PDF]
Jisuanji gongcheng, 2018 In the moving object detection methods base on low rank and sparse decomposition,the nuclear norm is not the best approximation of the rank function of the matrix,meanwhile,the spatial continuity of moving object is not to be considered.As a result,the ...
XUAN Xiao,YU Qin
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CoRR, 2017 Rank minimization methods have attracted considerable interest in various areas, such as computer vision and machine learning. The most representative work is nuclear norm minimization (NNM), which can recover the matrix rank exactly under some restricted and theoretical guarantee conditions.
Zhiyuan Zha +4 more
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Mathematics, 2022 The target detection ability of an infrared small target detection (ISTD) system is advantageous in many applications. The highly varied nature of the background image and small target characteristics make the detection process extremely difficult.
Sur Singh Rawat +4 more
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Singular Value Thresholding Algorithm for Wireless Sensor Network Localization
Mathematics, 2020 Wireless Sensor Networks (WSN) are of great current interest in the proliferation of technologies. Since the location of the sensors is one of the most interesting issues in WSN, the process of node localization is crucial for any WSN-based applications.
Yasmeen Nadhirah Ahmad Najib +2 more
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A Convex Collaborative Filtering Framework for Global Market Return Prediction
IEEE Access, 2021 A new convex collaborative filtering framework for global market return prediction is presented. The prediction problem is first phrased as a matrix completion problem assuming that the matrix of market returns is low rank.
Talal Al-Sulaiman, Ali Al-Matouq
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Rank aggregation via nuclear norm minimization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011 The process of rank aggregation is intimately intertwined with the structure of skew-symmetric matrices. We apply recent advances in the theory and algorithms of matrix completion to skew-symmetric matrices. This combination of ideas produces a new method for ranking a set of items.
David F. Gleich, Lek-Heng Lim
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Real Color Image Denoising Using t-Product- Based Weighted Tensor Nuclear Norm Minimization
IEEE Access, 2019 Color images can be seen as third-order tensors with column, row and color modes. Considering two inherent characteristics of a color image including the non-local self-similarity (NSS) and the cross-channel correlation, we extract non-local similar ...
Min Liu, Xinggan Zhang, Lan Tang
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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.
Karthik Mohan, Maryam Fazel
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IEEE Access, 2017 Low rank representation (LRR) is powerful for subspace clustering due to its strong ability in exploring low-dimensional subspace structures embedded in data.
Tao Zhang, Zhenmin Tang, Xiaobo Shen
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