Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds.
Journal Article (Journal Article)
Nonparametric Bayesian methods are employed to constitute a mixture of low-rank Gaussians, for data x ∈ ℝ N that are of high dimension N but are constrained to reside in a low-dimensional subregion of ℝ N . 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-sparsity properties. The proposed methodology is validated on several synthetic and real datasets.
Full Text
Duke Authors
Cited Authors
- Chen, M; Silva, J; Paisley, J; Wang, C; Dunson, D; Carin, L
Published Date
- December 2010
Published In
Volume / Issue
- 58 / 12
Start / End Page
- 6140 - 6155
PubMed ID
- 23894225
Pubmed Central ID
- PMC3721352
International Standard Serial Number (ISSN)
- 1053-587X
Digital Object Identifier (DOI)
- 10.1109/tsp.2010.2070796
Language
- eng