Variational Gaussian Process Dynamical Systems

2011/07/25 by Andreas C. Damianou, Andreas Damianou, Michalis K. Titsias +4 · 6 citations
Computer Science · Engineering · Mathematics · #58E30 #60G15 (Primary) #62-09 #Advanced Multi-Objective Optimization Algorithms #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Control Systems and Identification #FOS: Computer and information sciences #FOS: Mathematics #G.1.2 #G.3 #Gaussian Processes and Bayesian Inference #I.2.6 #I.5.4 #Machine Learning (stat.ML) #Probability (math.PR) #acm:58E30 #acm:60G15 #acm:62-09 #cs.AI #cs.CV #math.PR #msc:58E30 #msc:60G15 #msc:62-09 #stat.ML

paper · pdf · doi:10.48550/arxiv.1107.4985

16 pages, 19 figures

arxiv created 2011/07/25 · openalex publication_date 2011/07/25 · arxiv updated 2011/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

High dimensional time series are endemic in applications of machine learning such as robotics (sensor data), computational biology (gene expression data), vision (video sequences) and graphics (motion capture data). Practical nonlinear probabilistic approaches to this data are required. In this paper we introduce the variational Gaussian process dynamical system. Our work builds on recent variational approximations for Gaussian process latent variable models to allow for nonlinear dimensionality reduction simultaneously with learning a dynamical prior in the latent space. The approach also allows for the appropriate dimensionality of the latent space to be automatically determined. We demonstrate the model on a human motion capture data set and a series of high resolution video sequences.

Cited by

• Deep Jump Gaussian Processes for Surrogate Modeling of High-Dimensional Piecewise Continuous Functions
• Bayesian Nonparametric Dynamical Clustering of Time Series
• Integrative Analysis and Imputation of Multiple Data Streams via Deep Gaussian Processes
• Sparse Techniques for Regression in Deep Gaussian Processes
• Variational Inference: A Review for Statisticians
• A Variant of Gaussian Process Dynamical Systems

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