High-dimensional data fusion via joint manifold learning

Mark A. Davenport, Chinmay Hegde, Marco F. Duarte, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that acquire large amounts of very high-dimensional data. To cope with such a data deluge, manifold models are often developed that provide a powerful theoretical and algorithmic framework for capturing the intrinsic structure of data governed by a low-dimensional set of parameters.However, these models do not typically take into account dependencies among multiple sensors. We thus propose a new joint manifold framework for data ensembles that exploits such dependencies. We show that joint manifold structure can lead to improved performance for manifold learning. Additionally, we leverage recent results concerning random projections of manifolds to formulate a universal, network-scalable dimensionality reduction scheme that efficiently fuses the data from all sensors.

Original languageEnglish (US)
Title of host publicationManifold Learning and Its Applications - Papers from the AAAI Fall Symposium, Technical Report
Pages20-27
Number of pages8
VolumeFS-10-06
StatePublished - Dec 1 2010
Event2010 AAAI Fall Symposium - Arlington, VA, United States
Duration: Nov 11 2010Nov 13 2010

Other

Other2010 AAAI Fall Symposium
CountryUnited States
CityArlington, VA
Period11/11/1011/13/10

ASJC Scopus subject areas

  • Engineering(all)

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