Results 21 to 30 of about 256,120 (256)
Online Low Rank Matrix Completion
CoRR, 2022 We study the problem of {\em online} low-rank matrix completion with $\mathsf{M}$ users, $\mathsf{N}$ items and $\mathsf{T}$ rounds. In each round, the algorithm recommends one item per user, for which it gets a (noisy) reward sampled from a low-rank user-item preference matrix.
Soumyabrata Pal, Prateek Jain 0002
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Low-rank matrix decompositions for ab initio nuclear structure
Physics Letters B, 2021 The extension of ab initio quantum many-body theory to higher accuracy and larger systems is intrinsically limited by the handling of large data objects in form of wave-function expansions and/or many-body operators.
A. Tichai +5 more
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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
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On Low-Rank Hankel Matrix Denoising
IFAC-PapersOnLine, 2021 The low-complexity assumption in linear systems can often be expressed as rank deficiency in data matrices with generalized Hankel structure. This makes it possible to denoise the data by estimating the underlying structured low-rank matrix. However, standard low-rank approximation approaches are not guaranteed to perform well in estimating the noise ...
Yin, Mingzhou; id_orcid0000-0001-7583-5318 +1 more
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Local low-rank approach to nonlinear matrix completion
EURASIP Journal on Advances in Signal Processing, 2021 This paper deals with a problem of matrix completion in which each column vector of the matrix belongs to a low-dimensional differentiable manifold (LDDM), with the target matrix being high or full rank.
Ryohei Sasaki +3 more
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Robust low-rank matrix estimation [PDF]
The Annals of Statistics, 2018 Many results have been proved for various nuclear norm penalized estimators of the uniform sampling matrix completion problem. However, most of these estimators are not robust: in most of the cases the quadratic loss function and its modifications are used. We consider robust nuclear norm penalized estimators using two well-known robust loss functions:
Elsener, Andreas, van de Geer, Sara
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Adaptive quantile low-rank matrix factorization [PDF]
Pattern Recognition, 2020 Low-rank matrix factorization (LRMF) has received much popularity owing to its successful applications in both computer vision and data mining. By assuming noise to come from a Gaussian, Laplace or mixture of Gaussian distributions, significant efforts have been made on optimizing the (weighted) $L_1$ or $L_2$-norm loss between an observed matrix and ...
Shuang Xu, Chunxia Zhang, Jiangshe Zhang
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Enhanced Low-Rank Matrix Approximation [PDF]
IEEE Signal Processing Letters, 2016 5 pages, 2 figures.
Ankit Parekh, Ivan W. Selesnick
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Low‐rank updates of matrix square roots
Numerical Linear Algebra with Applications, 2023 AbstractModels in which the covariance matrix has the structure of a sparse matrix plus a low rank perturbation are ubiquitous in data science applications. It is often desirable for algorithms to take advantage of such structures, avoiding costly matrix computations that often require cubic time and quadratic storage.
Shany Shmueli +2 more
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Modifiable low‐rank approximation to a matrix
Numerical Linear Algebra with Applications, 2009 AbstractA truncated ULV decomposition (TULVD) of an m×n matrix X of rank k is a decomposition of the form X = ULVT+E, where U and V are left orthogonal matrices, L is a k×k non‐singular lower triangular matrix, and E is an error matrix. Only U,V, L, and ∥E∥F are stored, but E is not stored.
Jesse L. Barlow, Hasan Erbay
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