@inproceedings{aa9a08ccf07c4aaa90a5b4b63ac908cc,
title = "Variational dependent multi-output Gaussian Process dynamical systems",
abstract = "This paper presents a dependent multi-output Gaussian process (GP) for modeling complex dynamical systems. The outputs are dependent in this model, which is largely different from previous GP dynamical systems. We adopt convolved multi-output GPs to model the outputs, which are provided with a flexible multi-output covariance function. We adapt the variational inference method with inducing points for approximate posterior inference of latent variables. Conjugate gradient based optimization is used to solve parameters involved. Besides the temporal dependency, the proposed model also captures the dependency among outputs in complex dynamical systems. We evaluate the model on both synthetic and real-world data, and encouraging results are observed.",
keywords = "Dynamical system, Gaussian process, Multi-output modeling, Variational inference",
author = "Jing Zhao and Shiliang Sun",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 17th International Conference on Discovery Science, DS 2014 ; Conference date: 08-10-2014 Through 10-10-2014",
year = "2014",
doi = "10.1007/978-3-319-11812-3\_30",
language = "英语",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "350--361",
editor = "Sa{\v s}o D{\v z}eroski and Pan{\v c}e Panov and Dragi Kocev and Ljup{\v c}o Todorovski",
booktitle = "Discovery Science - 17th International Conference, DS 2014, Proceedings",
address = "德国",
}