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2010

Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds

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Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds
Nonparametric Bayesian methods are employed to constitute a mixture of low-rank Gaussians, for data x RN that are of high dimension N but are constrained to reside in a low-dimensional subregion of RN . The number of mixture components and their rank are inferred automatically from the data. The resulting algorithm can be used for learning manifolds and for reconstructing signals from manifolds, based on compressive sensing (CS) projection measurements. The statistical CS inversion is performed analytically. We derive the required number of CS random measurements needed for successful reconstruction, based on easily computed quantities, drawing on block
Minhua Chen, Jorge Silva, John William Paisley, Ch
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TSP
Authors Minhua Chen, Jorge Silva, John William Paisley, Chunping Wang, David B. Dunson, Lawrence Carin
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