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Found 5 papers, 2 papers with code
Scalable Regularised Joint Mixture Models
Building on developments at the intersection of unsupervised learning and regularised regression, we propose an approach for heterogeneous data that allows joint learning of (i) explicit multivariate feature distributions, (ii) high-dimensional regression models and (iii) latent group labels, with both (i) and (ii) specific to latent groups and both elements informing (iii).
On unsupervised projections and second order signals
Motivated by such applications, in this paper we ask whether linear projections can preserve differences in second order structure between latent groups.
Mixture of Conditional Gaussian Graphical Models for unlabelled heterogeneous populations in the presence of co-factors
We demonstrate on synthetic and real data how this method fulfils its goal and succeeds in identifying the sub-populations where the Mixtures of GGM are disrupted by the effect of the co-features.
Deterministic Approximate EM Algorithm; Application to the Riemann Approximation EM and the Tempered EM
In this paper, we introduce a theoretical framework, with state-of-the-art convergence guarantees, for any deterministic approximation of the E step.
Gaussian Graphical Model exploration and selection in high dimension low sample size setting
We demonstrate on synthetic data that, when the sample size is small, the two methods produce graphs with either too few or too many edges when compared to the real one.
