Results 11 to 20 of about 240,751 (277)
Individualized rank aggregation using nuclear norm regularization [PDF]
2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015 In recent years rank aggregation has received significant attention from the machine learning community. The goal of such a problem is to combine the (partially revealed) preferences over objects of a large population into a single, relatively consistent ordering of those objects.
Lu, Yu, Negahban, Sahand N.
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A Log-Det Heuristics for Covariance Matrix Estimation: The Analytic Setup
Stats, 2022 This paper studies a new nonconvex optimization problem aimed at recovering high-dimensional covariance matrices with a low rank plus sparse structure. The objective is composed of a smooth nonconvex loss and a nonsmooth composite penalty.
Enrico Bernardi, Matteo Farnè
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Robust Semisupervised Land-Use Classification Using Remote Sensing Data With Weak Labels
IEEE Access, 2022 This work develops robust semisupervised classifiers to tackle the three most challenging problems in land-use classification using remote sensing data, namely, information imbalance, label noise, and image uncertainty.
Rui Wang, Man-On Pun
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Double Structured Nuclear Norm-Based Matrix Decomposition for Saliency Detection
IEEE Access, 2020 Saliency detection aims at identifying the most important and informative area in a scene. Recently low rank matrix recovery (LR) theory becomes an effective tool for saliency detection.
Junxia Li, Ziyang Wang, Zefeng Pan
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Brazil and the TPNW: Brazilian Interests and the Promotion of the Norm of Nuclear Prohibition [PDF]
Contexto InternacionalThe Treaty on the Prohibition of Nuclear Weapons (TPNW) formalises the norm of nuclear prohibition and is presented as a way to fill a gap regarding nuclear disarmament.
Luiza Elena Januário, Raquel Gontijo
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Symmetric Tensor Nuclear Norms [PDF]
SIAM Journal on Applied Algebra and Geometry, 2017 25 ...
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A Direct Coarray Interpolation Approach for Direction Finding
Sensors, 2017 Sparse arrays have gained considerable attention in recent years because they can resolve more sources than the number of sensors. The coprime array can resolve O ( M N ) sources with only O ( M + N ) sensors, and is a popular sparse ...
Tao Chen, Muran Guo, Limin Guo
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Locality-Constrained Double Low-Rank Representation for Effective Face Hallucination
IEEE Access, 2016 Recently, position-patch-based face hallucination methods have received much attention, and obtained promising progresses due to their effectiveness and efficiency.
Guangwei Gao +5 more
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IEEE Access, 2020 Low-rank and sparse decomposition (LRSD) has attracted wide attention in video foreground-background separation and many other fields. However, the traditional LRSD methods have many tough problems, such as the problems of the low accuracy of the ...
Yongpeng Yang +3 more
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NON-LINEAR MULTI-FRAME IMAGE DENOISING USING WEIGHTED NUCLEAR NORM MINIMIZATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021 We address the problem of constructing single low noise image from a sequence of multiple noisy images. We use the approach based on finding and averaging similar blocks in the image and extend it to multiple images.
A. V. Nasonov +2 more
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