Gaussian Process Latent Variable Models for Dimensionality Reduction and Time Series Modeling
نویسنده
چکیده
Time series data of high dimensions are frequently encountered in fields like robotics, computer vision, economics and motion capture. In this survey paper we look first at Gaussian Process Latent Variable Model (GPLVM) which is a probabilistic nonlinear dimensionality reduction method. Further we discuss Gaussian Process Dynamical Model (GPDMs) which are based GPLVM. GPDM is a probabilistic approach to model high dimensional time series data in a low dimensional latent space with a dynamical model. We also discuss variational approximations of GPLVM and Variational Gaussian Process Dynamical System (VGPDS) which is a dynamical model based on these variational approximations.
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تاریخ انتشار 2012