With the aim of determining the connection between the indicators of body posture and latent structure of morphological variables in children aged 7 and 8 years, first and second grade of primary school, a set of 17 morphological measures and 12 body posture indicators were longitudinally applied to a sample of 110 boys and 114 girls. The latent structure of morphological variables in both sexe...

In this paper, we propose a new methodology to build latent variables that are optimal if a nonlinear model is used afterward. This method is based on Nonparametric Noise Estimation (NNE). NNE is providing an estimate of the variance of the noise between input and output variables. The linear projection that builds latent variables is optimized in order to minimize the NNE. We successfully test...

Latent variables often play an important role in improving the quality of the learned Bayesian networks and understanding the nature of interactions in the model. The dimensionality of latent variables has significant effect on the representation quality and complexity of the model. The maximum possible dimensionality of a latent variable is a Cartesian product of the state space of its Markov ...

We consider the problem of covariance matrix estimation in the presence of latent variables. Under suitable conditions, it is possible to learn the marginal covariance matrix of the observed variables via a tractable convex program, where the concentration matrix of the observed variables is decomposed into a sparse matrix (representing the graphical structure of the observed variables) and a l...

The Gaussian Process Latent Variable Model (GP-LVM) is a non-linear probabilistic method of embedding a high dimensional dataset in terms low dimensional ‘latent’ variables. In this paper we illustrate that maximum a posteriori (MAP) estimation of the latent variables and hyperparameters can be used for model selection and hence we can determine the optimal number or latent variables and the mo...

This is the first part of a two-parted report on development of a statistical learning algorithm for a latent variable model referred to as cooperative vector quantizer model. This part presents the theory and mathematical derivations of a variational Bayesian learning algorithm for the model. The model has general applications in the field of machine learning and signal processing. For example...