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A Theoretical Analysis of Joint Manifolds

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dc.contributor.author Davenport, Mark A.
Hegde, Chinmay
Duarte, Marco
Baraniuk, Richard G.
dc.date.accessioned 2009-12-15T21:01:18Z
dc.date.available 2009-12-15T21:01:18Z
dc.date.issued 2009-01
dc.identifier.uri http://hdl.handle.net/1911/27314
dc.description.abstract The 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.
dc.language.iso en_US
dc.relation.ispartofseries ;TREE0901
dc.subject sensor arrays
joint manifolds
dc.title A Theoretical Analysis of Joint Manifolds
dc.type Report
dc.type.dcmi Text
dc.identifier.citation M. A. Davenport, C. Hegde, M. Duarte and R. G. Baraniuk, "A Theoretical Analysis of Joint Manifolds," 2009.

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  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.