Manifold Learning With Contracting Observers for Data-Driven Time-Series Analysis

Analyzing signals arising from dynamical systems typically requires many modeling assumptions. In high dimensions, this modeling is particularly difficult due to the "curse of dimensionality." In this paper, we propose a method for building an intrinsic representation of such signals in a...

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Bibliographic Details
Main Authors: Shnitzer, Tal (Author), Talmon, Ronen (Author), Slotine, Jean-Jacques E (Author)
Other Authors: Massachusetts Institute of Technology. Nonlinear Systems Laboratory (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor), Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2022-08-18T18:34:26Z.
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