The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario
This work addresses a domain-specific challenge in handling ill-conditioned observational data for dimensionality reduction, representing an incremental improvement over existing methods.
The paper tackles the problem of learning low-dimensional representations from noisy and irregularly sampled longitudinal data using Gaussian Process Latent Variable Models, by augmenting variational bounds to include systematic samples of unseen observations, and demonstrates its usefulness on synthetic and human motion capture datasets.
The Dynamical Gaussian Process Latent Variable Models provide an elegant non-parametric framework for learning the low dimensional representations of the high-dimensional time-series. Real world observational studies, however, are often ill-conditioned: the observations can be noisy, not assuming the luxury of relatively complete and equally spaced like those in time series. Such conditions make it difficult to learn reasonable representations in the high dimensional longitudinal data set by way of Gaussian Process Latent Variable Model as well as other dimensionality reduction procedures. In this study, we approach the inference of Gaussian Process Dynamical Systems in Longitudinal scenario by augmenting the bound in the variational approximation to include systematic samples of the unseen observations. We demonstrate the usefulness of this approach on synthetic as well as the human motion capture data set.