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Unsupervised Regression with Applications to Nonlinear System Identification

Rahimi, Ali and Recht, Ben (2007) Unsupervised Regression with Applications to Nonlinear System Identification. In: Advances in Neural Information Processing Systems 19 (NIPS 2006). Advances in Neural Information Processing Systems. No.19. MIT Press , Cambridge, MA, pp. 1113-1120. ISBN 0-262-19568-2. https://resolver.caltech.edu/CaltechAUTHORS:20160314-160139280

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Abstract

We derive a cost functional for estimating the relationship between high-dimensional observations and the low-dimensional process that generated them with no input-output examples. Limiting our search to invertible observation functions confers numerous benefits, including a compact representation and no suboptimal local minima. Our approximation algorithms for optimizing this cost functional are fast and give diagnostic bounds on the quality of their solution. Our method can be viewed as a manifold learning algorithm that utilizes a prior on the low-dimensional manifold coordinates. The benefits of taking advantage of such priors in manifold learning and searching for the inverse observation functions in system identification are demonstrated empirically by learning to track moving targets from raw measurements in a sensor network setting and in an RFID tracking experiment.


Item Type:Book Section
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http://papers.nips.cc/paper/3039-unsupervised-regression-with-applications-to-nonlinear-system-identificationOrganizationPaper
Additional Information:© 2007 Massachusetts Institute of Technology.
Series Name:Advances in Neural Information Processing Systems
Issue or Number:19
Record Number:CaltechAUTHORS:20160314-160139280
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160314-160139280
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65342
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:30 Mar 2016 23:48
Last Modified:03 Oct 2019 09:46

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