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

Paper:

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

Received:
April 28, 2015
Accepted:
July 24, 2015
Online released:
September 20, 2015
Published:
September 20, 2015
Keywords:
gaussian process dynamical models, time-series data, visualization, supervised learning
Abstract

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.

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Last updated on May. 26, 2017