Generative Topographic Mapping (GTM) is a non-linear latent variable model that provides simultaneous visualization and clustering of high-dimensional data.

In this paper, we de ne an alternative variational formulation of GTM that provides a full Bayesian treatment to a Gaussian Pro ess (GP)-based variation of GTM.

Generative Topographic Mapping (GTM) is a non-linear la- tent variable model that provides simultaneous visualization and clus- tering of high-dimensional data.

This paper defines an alternative variational formulation of GTM that provides a full Bayesian treatment to a Gaussian Process (GP)-based variation ofGTM ...

Generative Topographic Mapping (GTM) is a nonlinear latent variable model introduced by Bishop et al. as a data visualization technique.

In this technical report, we provide the theoretical foundations of the reformulation of GTM-TT within the Variational Bayesian framework. This approach, in its ...

This Variational GTM was shown to limit the negative effect of data overfitting, improving on the performance of the standard GTM with GP prior, while retaining ...

Generative topographic mapping (GTM) is a manifold learning model for the simultaneous visualization and clustering of multivariate data.

In this paper, we define an alternative variational formulation of GTM that provides a full Bayesian treatment to a Gaussian Process (GP) - based variation of.

Generative Topographic Mapping (GTM) is a non-linear latent variable model that provides simultaneous visualization and clustering of high-dimensional data.