Search Results for author: Charles Bouveyron

Found 12 papers, 1 papers with code

Generalised Mutual Information: a Framework for Discriminative Clustering

no code implementations6 Sep 2023 Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron, Warith Harchaoui, Mickaël Leclercq, Arnaud Droit, Frédéric Precioso

In the last decade, recent successes in deep clustering majorly involved the Mutual Information (MI) as an unsupervised objective for training neural networks with increasing regularisations.

Clustering Deep Clustering

Another Point of View on Visual Speech Recognition

no code implementations Interspeech 2023 Baptiste Pouthier, Laurent Pilati, Giacomo Valenti, Charles Bouveyron, Frederic Precioso

Standard Visual Speech Recognition (VSR) systems directly process images as input features without any apriori link between raw pixel data and facial traits.

Landmark-based Lipreading speech-recognition +1

The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges

no code implementations14 Apr 2023 Rémi Boutin, Pierre Latouche, Charles Bouveyron

To understand those heterogeneous and complex data structures, clustering nodes into homogeneous groups as well as rendering a comprehensible visualisation of the data is mandatory.

Clustering Model Selection +2

Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism

no code implementations15 Feb 2023 Aude Sportisse, Hugo Schmutz, Olivier Humbert, Charles Bouveyron, Pierre-Alexandre Mattei

Semi-supervised learning is a powerful technique for leveraging unlabeled data to improve machine learning models, but it can be affected by the presence of ``informative'' labels, which occur when some classes are more likely to be labeled than others.

Data Augmentation

Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection

no code implementations7 Feb 2023 Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron, Mickaël Leclercq, Arnaud Droit, Frédéric Precioso

Feature selection in clustering is a hard task which involves simultaneously the discovery of relevant clusters as well as relevant variables with respect to these clusters.

Clustering feature selection +1

Generalised Mutual Information for Discriminative Clustering

1 code implementation12 Oct 2022 Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron, Warith Harchaoui, Mickaël Leclercq, Arnaud Droit, Frederic Precioso

In the last decade, recent successes in deep clustering majorly involved the mutual information (MI) as an unsupervised objective for training neural networks with increasing regularisations.

Clustering Deep Clustering

Unobserved classes and extra variables in high-dimensional discriminant analysis

no code implementations3 Feb 2021 Michael Fop, Pierre-Alexandre Mattei, Charles Bouveyron, Thomas Brendan Murphy

In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase.

Classification General Classification +2

DeepLTRS: A Deep Latent Recommender System based on User Ratings and Reviews

no code implementations1 Jan 2021 Dingge LIANG, Marco Corneli, Pierre Latouche, Charles Bouveyron

The underlying motivation is that, when a user scores only a few products, the texts used in the reviews represent a significant source of information.

Recommendation Systems

A Bayesian Fisher-EM algorithm for discriminative Gaussian subspace clustering

no code implementations8 Dec 2020 Nicolas Jouvin, Charles Bouveyron, Pierre Latouche

High-dimensional data clustering has become and remains a challenging task for modern statistics and machine learning, with a wide range of applications.

Image Denoising Methodology

Exact Dimensionality Selection for Bayesian PCA

no code implementations8 Mar 2017 Charles Bouveyron, Pierre Latouche, Pierre-Alexandre Mattei

We present a Bayesian model selection approach to estimate the intrinsic dimensionality of a high-dimensional dataset.

Model Selection

Bayesian Variable Selection for Globally Sparse Probabilistic PCA

no code implementations19 May 2016 Charles Bouveyron, Pierre Latouche, Pierre-Alexandre Mattei

To this end, using Roweis' probabilistic interpretation of PCA and a Gaussian prior on the loading matrix, we provide the first exact computation of the marginal likelihood of a Bayesian PCA model.

Model Selection Variable Selection

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