Results 11 to 20 of about 9,171,861 (296)
Multiscale Decomposition in Low-Rank Approximation [PDF]
IEEE Signal Processing Letters, 2017 In low-rank approximation methods, it is often assumed that the data matrix is composed of two globally low-rank and sparse matrices. Moreover, real data matrices often consist of local patterns in multiple scales. The conventional low-rank approximation techniques do not reveal the local patterns from the data matrices.
Abdolali, Maryam, Rahmati, Mohammad
openaire +3 more sources
A low-rank approximation of tensors and the topological group structure of invertible matrices
Учёные записки Казанского университета: Серия Физико-математические науки, 2018 By a tensor we mean an element of the tensor product of vector spaces over a field. Up to a choice of bases in factors of tensor products, every tensor may be coordinatized, i.e., represented as an array consisting of numbers.
R.N. Gumerov, A.S. Sharafutdinov
doaj +1 more source
Parameterized low-rank binary matrix approximation [PDF]
Data Mining and Knowledge Discovery, 2020 We provide a number of algorithmic results for the following family of problems: For a given binary m\times n matrix A and integer k, decide whether there is a "simple" binary matrix B which differs from A in at most k entries. For an integer r, the "simplicity" of B is characterized as follows.
Fedor V. Fomin +2 more
openaire +7 more sources
Low-rank approximation of analytic kernels
CoRR20 pages, 5 ...
Webb, Marcus; id_orcid
openaire +5 more sources
Randomized Methods for Dynamical Low-Rank Approximation
Journal of Computational PhysicsWe introduce novel dynamical low-rank methods for solving large-scale matrix differential equations, motivated by algorithms from randomized numerical linear algebra. In terms of performance (cost and accuracy), our methods overperform existing dynamical low-rank techniques.
Carrel, Benjamin
openaire +5 more sources
Low-Rank Approximation for Multiscale PDEs
Notices of the American Mathematical Society, 2022 The main theme of this article is the use of randomized SVD solvers to exploit the low-rank features of multiscale PDEs. We describe two strategies both of which are divided into “offline” and “online” stages.
Ke Chen +4 more
openaire +3 more sources
Bounded Matrix Low Rank Approximation [PDF]
2012 IEEE 12th International Conference on Data Mining, 2012 Matrix lower rank approximations such as non-negative matrix factorization (NMF) have been successfully used to solve many data mining tasks. In this paper, we propose a new matrix lower rank approximation called Bounded Matrix Low Rank Approximation (BMA) which imposes a lower and an upper bound on every element of a lower rank matrix that best ...
Ramakrishnan Kannan +2 more
openaire +4 more sources
Low Rank Tensor Decompositions and Approximations
Journal of the Operations Research Society of China, 2023 AbstractThere exist linear relations among tensor entries of low rank tensors. These linear relations can be expressed by multi-linear polynomials, which are called generating polynomials. We use generating polynomials to compute tensor rank decompositions and low rank tensor approximations.
Jiawang Nie, Li Wang, Zequn Zheng
openaire +3 more sources
Low-Rank Approximation of Tensors [PDF]
, 2015 28 pages, 5 ...
Friedland, Shmuel, Tammali, Venu
openaire +2 more sources
Approximation Algorithms for $\ell_0$-Low Rank Approximation
CoRR, 2017 We study the $\ell_0$-Low Rank Approximation Problem, where the goal is, given an $m \times n$ matrix $A$, to output a rank-$k$ matrix $A'$ for which $\|A'-A\|_0$ is minimized. Here, for a matrix $B$, $\|B\|_0$ denotes the number of its non-zero entries.
Bringmann, K. ; https://orcid.org/0000-0003-1356-5177 +2 more
openaire +4 more sources