9 citations · 9 across the 5 of their papers we have counts for
6 papers
Scalable Regularised Joint Mixture Models
Thomas Lartigue, Sach Mukherjee
In many applications, data can be heterogeneous in the sense of spanning latent groups with different underlying distributions. When predictive models are applied to such data the…
On unsupervised projections and second order signals
Thomas Lartigue, Sach Mukherjee
Linear projections are widely used in the analysis of high-dimensional data. In unsupervised settings where the data harbour latent classes/clusters, the question of whether class…
Mixture of Conditional Gaussian Graphical Models for unlabelled heterogeneous populations in the presence of co-factors
Thomas Lartigue, Stanley Durrleman, Stéphanie Allassonnière
Conditional correlation networks, within Gaussian Graphical Models (GGM), are widely used to describe the direct interactions between the components of a random vector. In the case…
Gaussian Graphical Model exploration and selection in high dimension low sample size setting
Thomas Lartigue, Simona Bottani, Stephanie Baron +3
Gaussian Graphical Models (GGM) are often used to describe the conditional correlations between the components of a random vector. In this article, we compare two families of GGM i…
Deterministic Approximate EM Algorithm; Application to the Riemann Approximation EM and the Tempered EM
Thomas Lartigue, Stanley Durrleman, Stéphanie Allassonnière
The Expectation Maximisation (EM) algorithm is widely used to optimise non-convex likelihood functions with latent variables. Many authors modified its simple design to fit more sp…
Regularized joint mixture models
Konstantinos Perrakis, Thomas Lartigue, Frank Dondelinger +1
Regularized regression models are well studied and, under appropriate conditions, offer fast and statistically interpretable results. However, large data in many applications are h…