A spatio-temporal extension to Isomap nonlinear dimension reduction

  • Authors:
  • Odest Chadwicke Jenkins;Maja J. Matarić

  • Affiliations:
  • University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA

  • Venue:
  • ICML '04 Proceedings of the twenty-first international conference on Machine learning
  • Year:
  • 2004

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Abstract

We present an extension of Isomap nonlinear dimension reduction (Tenenbaum et al., 2000) for data with both spatial and temporal relationships. Our method, ST-Isomap, augments the existing Isomap framework to consider temporal relationships in local neighborhoods that can be propagated globally via a shortest-path mechanism. Two instantiations of ST-Isomap are presented for sequentially continuous and segmented data. Results from applying ST-Isomap to real-world data collected from human motion performance and humanoid robot teleoperation are also presented.