Results 21 to 30 of about 282,377 (316)
Kleinian groups and the rank problem [PDF]
Geometry & Topology, 2005 Published by Geometry and Topology at http://www.maths.warwick.ac.uk/gt/GTVol9/paper12.abs ...
Kapovich, Ilya, Weidmann, Richard
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Empirical and Philosophical Problems with the Subspecies Rank
Ecology and Evolution, 2022 Species-level taxonomy is derived from methodological sources (data and techniques) that assess the existence of spatio-temporal evolutionary lineages via various species concepts. These concepts determine if observed lineages are independent given a particular methodology supposedly connected to ontology, which relates the metaphysical concept to what
Frank T. Burbrink +6 more
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IEEE Access, 2018 Sparse and low-rank decomposition (SLRD) poses a big challenge in many fields. The existing methods are used to solve SLRD problem via formulating approximations of sparse and low-rank matrices.
Zhenzhen Yang, Zhen Yang, Deren Han
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Quantum-Inspired Hierarchy for Rank-Constrained Optimization
PRX Quantum, 2022 Many problems in information theory can be reduced to optimizations over matrices, where the rank of the matrices is constrained. We establish a link between rank-constrained optimization and the theory of quantum entanglement.
Xiao-Dong Yu +3 more
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The augmented lagrange multipliers method for matrix completion from corrupted samplings with application to mixed Gaussian-impulse noise removal. [PDF]
PLoS ONE, 2014 This paper studies the problem of the restoration of images corrupted by mixed Gaussian-impulse noise. In recent years, low-rank matrix reconstruction has become a research hotspot in many scientific and engineering domains such as machine learning ...
Fan Meng, Xiaomei Yang, Chenghu Zhou
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Low-rank sparse subspace clustering with a clean dictionary
Journal of Algorithms & Computational Technology, 2021 Low-Rank Representation (LRR) and Sparse Subspace Clustering (SSC) are considered as the hot topics of subspace clustering algorithms. SSC induces the sparsity through minimizing the l 1 -norm of the data matrix while LRR promotes a low-rank structure ...
Cong-Zhe You, Zhen-Qiu Shu, Hong-Hui Fan
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On the Complexity of the Rank Syndrome Decoding Problem [PDF]
IEEE Transactions on Information Theory, 2016 In this paper we propose two new generic attacks on the Rank Syndrome Decoding (RSD) problem Let $C$ be a random $[n,k]$ rank code over $GF(q^m)$ and let $y=x+e$ be a received word such that $x \in C$ and the $Rank(e)=r$. The first attack is combinatorial and permits to recover an error $e$ of rank weight $r$ in $min(O((n-k)^3m^3q^{r\lfloor\frac{km}{n}\
Gaborit, Philippe +2 more
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Entropy, 2021 Feature selection is known to be an applicable solution to address the problem of high dimensionality in software defect prediction (SDP). However, choosing an appropriate filter feature selection (FFS) method that will generate and guarantee optimal ...
Abdullateef O. Balogun +7 more
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A New Technique in Rank Metric Code-Based Encryption
Cryptography, 2018 We propose a rank metric codes based encryption based on the hard problem of rank syndrome decoding problem. We propose a new encryption with a public key matrix by considering the adding of a random distortion matrix over F q m of full column ...
Terry Shue Chien Lau, Chik How Tan
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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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