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pro vyhledávání: '"ANG, ANDERSEN"'
When applying nonnegative matrix factorization (NMF), generally the rank parameter is unknown. Such rank in NMF, called the nonnegative rank, is usually estimated heuristically since computing the exact value of it is NP-hard. In this work, we propos
Externí odkaz:
http://arxiv.org/abs/2407.00706
Autor:
Esposito, Flavia, Ang, Andersen
Nonnegative Matrix Factorization (NMF) is the problem of approximating a given nonnegative matrix M through the conic combination of two nonnegative low-rank matrices W and H. Traditionally NMF is tackled by optimizing a specific objective function e
Externí odkaz:
http://arxiv.org/abs/2405.12823
We study estimation of piecewise smooth signals over a graph. We propose a $\ell_{2,0}$-norm penalized Graph Trend Filtering (GTF) model to estimate piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness across the nodes. We p
Externí odkaz:
http://arxiv.org/abs/2304.05223
We study the combination of proximal gradient descent with multigrid for solving a class of possibly nonsmooth strongly convex optimization problems. We propose a multigrid proximal gradient method called MGProx, which accelerates the proximal gradie
Externí odkaz:
http://arxiv.org/abs/2302.04077
We consider the problem of projecting a vector onto the so-called k-capped simplex, which is a hyper-cube cut by a hyperplane. For an n-dimensional input vector with bounded elements, we found that a simple algorithm based on Newton's method is able
Externí odkaz:
http://arxiv.org/abs/2110.08471
Publikováno v:
Numerical Linear Algebra with Applications, e2373, 2021
This paper is concerned with improving the empirical convergence speed of block-coordinate descent algorithms for approximate nonnegative tensor factorization (NTF). We propose an extrapolation strategy in-between block updates, referred to as heuris
Externí odkaz:
http://arxiv.org/abs/2001.04321
Publikováno v:
Neural Computation 31 (2), pp. 417-439, 2019
In this paper, we propose a general framework to accelerate significantly the algorithms for nonnegative matrix factorization (NMF). This framework is inspired from the extrapolation scheme used to accelerate gradient methods in convex optimization a
Externí odkaz:
http://arxiv.org/abs/1805.06604
Autor:
ANG, ANDERSEN1 andersen.ang@soton.ac.uk, DE STERCK, HANS2 hans.desterck@uwaterloo.ca, VAVASIS, STEPHEN3 vavasis@uwaterloo.ca
Publikováno v:
SIAM Journal on Optimization. 2024, Vol. 34 Issue 3, p2788-2820. 33p.
Autor:
Ang, Andersen Man Shun1 manshun.ang@umons.ac.be, Gillis, Nicolas1 nicolas.gillis@umons.ac.be
Publikováno v:
Neural Computation. Feb2019, Vol. 31 Issue 2, p417-439. 23p. 6 Charts, 5 Graphs.
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