Výsledky vyhledávání - "Arnaud Vandaele"

Matrix-wise $$\ell _0$$-constrained sparse nonnegative least squares

Autor: Nicolas Nadisic, Jeremy E. Cohen, Arnaud Vandaele, Nicolas Gillis

Publikováno v: Machine Learning. 111:4453-4495

Nonnegative least squares problems with multiple right-hand sides (MNNLS) arise in models that rely on additive linear combinations. In particular, they are at the core of most nonnegative matrix factorization algorithms and have many applications. T

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd951d7def6706f8d4b2513b5461723f
https://doi.org/10.1007/s10994-022-06260-2

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Exact Biobjective k-Sparse Nonnegative Least Squares

Autor: Nicolas Nadisic, Arnaud Vandaele, Nicolas Gillis, Jeremy E. Cohen

Publikováno v: EUSIPCO 2021-29th European Signal Processing Conference
EUSIPCO 2021-29th European Signal Processing Conference, Aug 2021, virtual, France. pp.1-5

International audience; The k-sparse nonnegative least squares (NNLS) problem is a variant of the standard least squares problem, where the solution is constrained to be nonnegative and to have at most k nonzero entries. Several methods exist to tack

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0f985ce4face0ce6b3d8591f80f3c4e
https://hal.archives-ouvertes.fr/hal-03439451/file/2021-ArboPareto-EUSIPCO.pdf

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Nonnegative Unimodal Matrix Factorization

Autor: Hans De Sterck, Nicolas Gillis, Arnaud Vandaele, Andersen Man Shun Ang

Publikováno v: ICASSP

We introduce a new Nonnegative Matrix Factorization (NMF) model called Nonnegative Unimodal Matrix Factorization (NuMF), which adds on top of NMF the unimodal condition on the columns of the basis matrix. NuMF finds applications for example in analyt

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::31bc501f9e839c4ec154bc6487382f18
https://doi.org/10.1109/icassp39728.2021.9414631

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Sparse Separable Nonnegative Matrix Factorization

Autor: Jeremy E. Cohen, Nicolas Gillis, Nicolas Nadisic, Arnaud Vandaele

Publikováno v: ECML PKDD 2020-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
ECML PKDD 2020-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2020, Ghent, Belgium. pp.1-20
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Machine Learning and Knowledge Discovery in Databases
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030676575
ECML/PKDD (1)
Machine Learning and Knowledge Discovery in Databases-European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part I

We propose a new variant of nonnegative matrix factorization (NMF), combining separability and sparsity assumptions. Separability requires that the columns of the first NMF factor are equal to columns of the input matrix, while sparsity requires that

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4958e3af71c844c44468374fa315c89e
https://hal.science/hal-02869490

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Exact Sparse Nonnegative Least Squares

Autor: Nicolas Gillis, Nicolas Nadisic, Jeremy E. Cohen, Arnaud Vandaele

Publikováno v: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2020, Barcelona, France. pp.5395-5399, ⟨10.1109/ICASSP40776.2020.9053295⟩
ICASSP

International audience; We propose a novel approach to solve exactly the sparse nonnega-tive least squares problem, under hard 0 sparsity constraints. This approach is based on a dedicated branch-and-bound algorithm. This simple strategy is able to c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42006d715efa53d8a36b0c3bd0c433cb

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Near-Convex Archetypal Analysis

Autor: Pierre De Handschutter, Xavier Siebert, Nicolas Gillis, Arnaud Vandaele

Publikováno v: IEEE Signal Processing Letters

Nonnegative matrix factorization (NMF) is a widely used linear dimensionality reduction technique for nonnegative data. NMF requires that each data point is approximated by a convex combination of basis elements. Archetypal analysis (AA), also referr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::32a1927c7ca544795a21ee254737c164

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Efficient and Non-Convex Coordinate Descent for Symmetric Nonnegative Matrix Factorization

Autor: Inderjit S. Dhillon, Qi Lei, Kai Zhong, Nicolas Gillis, Arnaud Vandaele

Publikováno v: IEEE Transactions on Signal Processing. 64:5571-5584

Given a symmetric nonnegative matrix $A$ , symmetric nonnegative matrix factorization (symNMF) is the problem of finding a nonnegative matrix $H$ , usually with much fewer columns than $A$ , such that $A \approx HH^T$ . SymNMF can be used for data an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::549eeb6c803231ec04f2a46eff95b6b9
https://doi.org/10.1109/tsp.2016.2591510

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Wetting dynamics of polydimethylsiloxane mixtures on a poly(ethylene terephthalate) fiber

Autor: Yichuan Zhang, Joël De Coninck, Arnaud Vandaele, David Seveno

Publikováno v: Journal of colloid and interface science. 525

HYPOTHESIS: The wetting dynamics of liquids with identical surface tensions are mostly controlled by their viscosities. We therefore hypothesized that the wetting dynamics of one- (pure liquid) and two-component (mixture) polydimethylsiloxane (PDMS)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::393c6c8ec4cd3e5fe122e7ebf86e78a7
https://pubmed.ncbi.nlm.nih.gov/29705594

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Algorithms for positive semidefinite factorization

Autor: Nicolas Gillis, Arnaud Vandaele, François Glineur

Publikováno v: Computational Optimization and Applications

This paper considers the problem of positive semidefinite factorization (PSD factorization), a generalization of exact nonnegative matrix factorization. Given an $m$-by-$n$ nonnegative matrix $X$ and an integer $k$, the PSD factorization problem cons

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Towards an efficient resolution of printing problems

Autor: Arnaud Vandaele, Daniel Tuyttens

Publikováno v: Discrete Optimization. 14:126-146

This paper addresses two different problems coming from the printing industry: the label printing problem and the cover printing problem. In both cases, the problem consists in the assignment of a fixed number of labels on different templates in orde

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4463f6af4bd0c16df2aa0d0238211142
https://doi.org/10.1016/j.disopt.2014.09.001

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