A Theoretical Analysis of Joint Manifolds

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dc.contributor.authorDavenport, Mark A.
dc.contributor.authorHegde, Chinmay
dc.contributor.authorDuarte, Marco
dc.contributor.authorBaraniuk, Richard G.
dc.date.accessioned2009-12-15T21:01:18Z
dc.date.available2009-12-15T21:01:18Z
dc.date.issued2009-01
dc.description.abstractThe emergence of low-cost sensor architectures for diverse modalities has made it possible to deploy sensor arrays that capture a single event from a large number of vantage points and using multiple modalities. In many scenarios, these sensors acquire very high-dimensional data such as audio signals, images, and video. To cope with such high-dimensional data, we typically rely on low-dimensional models. Manifold models provide a particularly powerful model that captures the structure of high-dimensional data when it is 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 simple algorithms can exploit the joint manifold structure to improve their performance on standard signal processing applications. Additionally, recent results concerning dimensionality reduction for manifolds enable us to formulate a network-scalable data compression scheme that uses random projections of the sensed data. This scheme efficiently fuses the data from all sensors through the addition of such projections, regardless of the data modalities and dimensions.en_US
dc.identifier.citationM. A. Davenport, C. Hegde, M. Duarte and R. G. Baraniuk, "A Theoretical Analysis of Joint Manifolds," 2009.
dc.identifier.urihttps://hdl.handle.net/1911/27314
dc.language.isoeng
dc.relation.IsPartOfSeriesRice University ECE Department Technical Report TREE0901
dc.subjectsensor arraysen_US
dc.subjectjoint manifoldsen_US
dc.titleA Theoretical Analysis of Joint Manifoldsen_US
dc.typeReport
dc.type.dcmiText
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