Results 41 to 50 of about 14,259,947 (309)
Nonconvex Low Tubal Rank Tensor Minimization
IEEE Access, 2019 In the sparse vector recovery problem, the L0-norm can be approximated by a convex function or a nonconvex function to achieve sparse solutions. In the low-rank matrix recovery problem, the nonconvex matrix rank can be replaced by a convex function or a ...
Yaru Su, Xiaohui Wu, Genggeng Liu
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Low-Rank Approximation of Tensors [PDF]
, 2015 28 pages, 5 ...
Friedland, Shmuel, Tammali, Venu
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IEEE Access, 2023 Recognizing the multiple categories of an high-resolution (HR) aerial photos is an indispensable technique in geoscience and remote sensing. In this work, a perceptual low-rank algorithm combined with a geometry-preserving feature selection (FS) is ...
Junwu Zhou, Fuji Ren
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Multiview Subspace Clustering via Low-Rank Symmetric Affinity Graph
IEEE Transactions on Neural Networks and Learning Systems, 2023 Multiview subspace clustering (MVSC) has been used to explore the internal structure of multiview datasets by revealing unique information from different views.
Wei Lan +6 more
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Low rank multivariate regression [PDF]
Electronic Journal of Statistics, 2011 We consider in this paper the multivariate regression problem, when the target regression matrix $A$ is close to a low rank matrix. Our primary interest in on the practical case where the variance of the noise is unknown. Our main contribution is to propose in this setting a criterion to select among a family of low rank estimators and prove a non ...
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The Annals of Statistics, 2023 This paper studies inference in linear models with a high-dimensional parameter matrix that can be well-approximated by a ``spiked low-rank matrix.'' A spiked low-rank matrix has rank that grows slowly compared to its dimensions and nonzero singular values that diverge to infinity.
Chernozhukov, Victor +3 more
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Compressing Transformers: Features Are Low-Rank, but Weights Are Not!
AAAI Conference on Artificial Intelligence, 2023 Transformer and its variants achieve excellent results in various computer vision and natural language processing tasks, but high computational costs and reliance on large training datasets restrict their deployment in resource-constrained settings.
Hao Yu, Jianxin Wu
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Materiali in TehnologijeIn this study, a ceramic proppant for hydraulic fracturing in oil and gas extraction was prepared by sintering secondary aluminum ash as the main raw material, kaolin as the auxiliary material and manganese dioxide as the additive.
Yongming Zeng +6 more
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Low-Rank Tensor Thresholding Ridge Regression
IEEE Access, 2019 In the area of subspace clustering, methods combining self-representation and spectral clustering are predominant in recent years. For dealing with tensor data, most existing methods vectorize them into vectors and lose most of the spatial information ...
Kailing Guo +3 more
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Low-Rank Matrix Factorization Method for Multiscale Simulations: A Review
IEEE Open Journal of Antennas and Propagation, 2021 In this paper, a review of the low-rank factorization method is presented, with emphasis on their application to multiscale problems. Low-rank matrix factorization methods exploit the rankdeficient nature of coupling impedance matrix blocks between two ...
Mengmeng Li +5 more
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