Search Results for author: Valentin Leplat

Found 9 papers, 5 papers with code

Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models

1 code implementation27 Mar 2024 Jeremy E. Cohen, Valentin Leplat

However, from a practical perspective, the choice of regularizers and regularization coefficients, as well as the design of efficient algorithms, is challenging because of the multifactor nature of these models and the lack of theory to back these choices.

Dimensionality Reduction

Block Majorization Minimization with Extrapolation and Application to $β$-NMF

1 code implementation12 Jan 2024 Le Thi Khanh Hien, Valentin Leplat, Nicolas Gillis

We propose a Block Majorization Minimization method with Extrapolation (BMMe) for solving a class of multi-convex optimization problems.

Deep Nonnegative Matrix Factorization with Beta Divergences

2 code implementations15 Sep 2023 Valentin Leplat, Le Thi Khanh Hien, Akwum Onwunta, Nicolas Gillis

Deep Nonnegative Matrix Factorization (deep NMF) has recently emerged as a valuable technique for extracting multiple layers of features across different scales.

NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer

2 code implementations29 Sep 2022 Valentin Leplat, Daniil Merkulov, Aleksandr Katrutsa, Daniel Bershatsky, Olga Tsymboi, Ivan Oseledets

Classical machine learning models such as deep neural networks are usually trained by using Stochastic Gradient Descent-based (SGD) algorithms.

Nonnegative Tucker Decomposition with Beta-divergence for Music Structure Analysis of Audio Signals

2 code implementations27 Oct 2021 Axel Marmoret, Florian Voorwinden, Valentin Leplat, Jérémy E. Cohen, Frédéric Bimbot

Nonnegative Tucker decomposition (NTD), a tensor decomposition model, has received increased interest in the recent years because of its ability to blindly extract meaningful patterns, in particular in Music Information Retrieval.

Information Retrieval Music Information Retrieval +2

Multiplicative Updates for NMF with $β$-Divergences under Disjoint Equality Constraints

no code implementations30 Oct 2020 Valentin Leplat, Nicolas Gillis, Jérôme Idier

In this paper, we introduce a general framework to design multiplicative updates (MU) for NMF based on $\beta$-divergences ($\beta$-NMF) with disjoint equality constraints, and with penalty terms in the objective function.

Multi-Resolution Beta-Divergence NMF for Blind Spectral Unmixing

no code implementations8 Jul 2020 Valentin Leplat, Nicolas Gillis, Cédric Févotte

We show on numerical experiments that the MU are able to obtain high resolutions in both dimensions on two applications: (1) blind unmixing of audio spectrograms: to the best of our knowledge, this is the first time a coupled NMF model is used in this context, and (2) the fusion of hyperspectral and multispectral images: we show that the MU compete favorable with state-of-the-art algorithms in particular in the presence of non-Gaussian noise.

blind source separation Hyperspectral Unmixing

Blind Audio Source Separation with Minimum-Volume Beta-Divergence NMF

no code implementations4 Jul 2019 Valentin Leplat, Nicolas Gillis, Man Shun Ang

Considering a mixed signal composed of various audio sources and recorded with a single microphone, we consider on this paper the blind audio source separation problem which consists in isolating and extracting each of the sources.

Audio Source Separation

Distributionally Robust and Multi-Objective Nonnegative Matrix Factorization

no code implementations30 Jan 2019 Nicolas Gillis, Le Thi Khanh Hien, Valentin Leplat, Vincent Y. F. Tan

We propose to use Lagrange duality to judiciously optimize for a set of weights to be used within the framework of the weighted-sum approach, that is, we minimize a single objective function which is a weighted sum of the all objective functions.

Dimensionality Reduction

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