Robust Matrix Completion via Maximum Correntropy Criterion and Half-Quadratic Optimization

He, Yicong; Wang, Fei; Li, Yingsong; Qin, Jing; Chen, Badong

doi:10.1109/tsp.2019.2952057

Cited by 65 publications

(26 citation statements)

References 47 publications

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“…As studied in [29,30], the assembly performance is evaluated by the weighted mean square of assembly deviation δw, which is defined as (20) where Q ∈ R 6×6 is a weighted coefficient matrix which represents the influence of different deviation items on the quality loss. The larger the coefficient related to the assembly deviation is, the more attention is paid to the accuracy index in assembly.…”

Section: Sparse Optimization For Dimensional Adjustment a Problem Formulation Of Dimensional Adjustmentmentioning

confidence: 99%

Link Adjustment for Assembly Deviation Control of Extendible Support Structures via Sparse Optimization

Guo

et al. 2021

IEEE Access

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The assembly accuracy of the extendible support structure is of importance for the imaging capability of synthetic aperture radar antennas. In general, due to manufacturing imperfections and installation variations, its assembly accuracy will be inevitably degraded. Therefore, controlling the assembly deviation is highly concerned in practice. To meet the accuracy requirement and make "the control" more efficient, this study proposes a novel method to quantitatively conduct dimensional adjustment of links for extendible support structures of synthetic aperture radar antennas. Since the extendible support structure is generally over-constrained in the deployed configuration, the relationship between the assembly deviation and the variation sources is first derived by means of equivalent transformation. Based on the mathematical expression of assembly deviation, an inequality constrained sparse optimization model for quantitatively resizing links is formulated. Then, an efficient algorithm integrating the alternating direction method of multipliers and binary search is developed to solve the above optimization model, thereby acquiring the optimal combination of link adjustment. Finally, numerical case studies are carried out to demonstrate the effectiveness of the proposed method in Matlab, which show that it can not only achieve satisfactory performance in prediction but also significantly improve the assembly efficiency.

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Section: Sparse Optimization For Dimensional Adjustment a Problem Formulation Of Dimensional Adjustmentmentioning

confidence: 99%

Link Adjustment for Assembly Deviation Control of Extendible Support Structures via Sparse Optimization

Guo

et al. 2021

IEEE Access

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“…Recently, correntropy has been widely studied in the fields of machine learning and computer vision [23][24][25][26][27][28]. One of its important advantages is that it can effectively deal with data containing with non-Gaussian noise or outliers using the information theoretical learning.…”

Section: Correntropymentioning

confidence: 99%

Correntropy-based dual graph regularized nonnegative matrix factorization with Lp smoothness for data representation

Shu

Weng

et al. 2021

Appl Intell

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Nonnegative matrix factorization methods have been widely used in many applications in recent years. However, the clustering performances of such methods may deteriorate dramatically in the presence of non-Gaussian noise or outliers. To overcome this problem, in this paper, we propose correntropy-based dual graph regularized NMF with L P smoothness (CDNMFS) for data representation. Specifically, we employ correntropy instead of the Euclidean norm to measure the incurred reconstruction error. Furthermore, we explore the geometric structures of both the input data and the feature space and impose an L p norm constraint to obtain an accurate solution. In addition, we introduce an efficient optimization scheme for the proposed model and present its convergence analysis. Experimental results on several image datasets demonstrate the superiority of the proposed CDNMFS method.

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“…given a finite number of samples {(x i , y i )} n i=1 , and accordingly the MCC is equivalent to minimizing [36]. Recent works applied MCC for a wide range of important applications, including face recognition, matrix completion, and low-rank matrix decomposition [27,28,[36][37][38][39], which is shown to be highly effective.…”

Section: Maximum Correntropy Criterion (Mcc)mentioning

confidence: 99%

Hyper RPCA: Joint Maximum Correntropy Criterion and Laplacian Scale Mixture Modeling On-the-Fly for Moving Object Detection

Shao¹,

Pu²,

Zhou³

et al. 2020

Preprint

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Moving object detection is critical for automated video analysis in many vision-related tasks, such as surveillance tracking, video compression coding, etc. Robust Principal Component Analysis (RPCA), as one of the most popular moving object modelling methods, aims to separate the temporallyvarying (i.e., moving) foreground objects from the static background in video, assuming the background frames to be lowrank while the foreground to be spatially sparse. Classic RPCA imposes sparsity of the foreground component using 1-norm, and minimizes the modeling error via 2-norm. We show that such assumptions can be too restrictive in practice, which limits the effectiveness of the classic RPCA, especially when processing videos with dynamic background, camera jitter, camouflaged moving object, etc. In this paper, we propose a novel RPCAbased model, called Hyper RPCA, to detect moving objects on the fly. Different from classic RPCA, the proposed Hyper RPCA jointly applies the maximum correntropy criterion (MCC) for the modeling error, and Laplacian scale mixture (LSM) model for foreground objects. Extensive experiments have been conducted, and the results demonstrate that the proposed Hyper RPCA has competitive performance for foreground detection to the stateof-the-art algorithms on several well-known benchmark datasets.

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Robust Matrix Completion via Maximum Correntropy Criterion and Half-Quadratic Optimization

Cited by 65 publications

References 47 publications

Link Adjustment for Assembly Deviation Control of Extendible Support Structures via Sparse Optimization

Link Adjustment for Assembly Deviation Control of Extendible Support Structures via Sparse Optimization

Correntropy-based dual graph regularized nonnegative matrix factorization with Lp smoothness for data representation

Hyper RPCA: Joint Maximum Correntropy Criterion and Laplacian Scale Mixture Modeling On-the-Fly for Moving Object Detection

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