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A spatio-temporal extension to Isomap nonlinear dimension reduction

Published: 04 July 2004 Publication History

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.

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  1. A spatio-temporal extension to Isomap nonlinear dimension reduction

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    cover image ACM Other conferences
    ICML '04: Proceedings of the twenty-first international conference on Machine learning
    July 2004
    934 pages
    ISBN:1581138385
    DOI:10.1145/1015330
    • Conference Chair:
    • Carla Brodley
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 04 July 2004

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