JACIII Vol.19 No.5 pp. 688-696
doi: 10.20965/jaciii.2015.p0688


Visualizing State of Time-Series Data by Supervised Gaussian Process Dynamical Models

Nobuhiko Yamaguchi

Faculty of Science and Engineering, Saga University
1 Honjo, Saga 840-8502, Japan

April 28, 2015
July 24, 2015
September 20, 2015
gaussian process dynamical models, time-series data, visualization, supervised learning
Gaussian Process Dynamical Models (GPDMs) constitute a nonlinear dimensionality reduction technique that provides a probabilistic representation of time series data in terms of Gaussian process priors. In this paper, we report a method based on GPDMs to visualize the states of time-series data. Conventional GPDMs are unsupervised, and therefore, even when the labels of data are available, it is not possible to use this information. To overcome the problem, we propose a supervised GPDM (S-GPDM) that utilizes both the data and their corresponding labels. We demonstrate experimentally that the S-GPDM can locate related motion data closer together than conventional GPDMs.
Cite this article as:
N. Yamaguchi, “Visualizing State of Time-Series Data by Supervised Gaussian Process Dynamical Models,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.5, pp. 688-696, 2015.
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