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...
| Main Authors: | , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/132143 |