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ABSTRACT
We present an extension of Isomap nonlinear dimension reduction
\cite{Tenenbaumetal2000} 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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