Robust sparse embedding and reconstruction via dictionary learning

Konstantinos Slavakis, Georgios B Giannakis, Geert Leus

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

7 Scopus citations

Abstract

A novel approach is developed for nonlinear compression and reconstruction of high-or even infinite-dimensional signals living on a smooth but otherwise unknown manifold. Compression is effected through affine embeddings to lower-dimensional spaces. These embeddings are obtained via linear regression and bilinear dictionary learning algorithms that leverage manifold smoothness as well as sparsity of the affine model and its residuals. The emergent unifying framework is general enough to encompass known locally linear embedding and compressive sampling approaches to dimensionality reduction. Emphasis is placed on reconstructing high-dimensional data from their low-dimensional embeddings. Preliminary tests demonstrate the analytical claims, and their potential to (de)compressing synthetic and real data.

Original languageEnglish (US)
Title of host publication2013 47th Annual Conference on Information Sciences and Systems, CISS 2013
DOIs
StatePublished - Aug 20 2013
Event2013 47th Annual Conference on Information Sciences and Systems, CISS 2013 - Baltimore, MD, United States
Duration: Mar 20 2013Mar 22 2013

Publication series

Name2013 47th Annual Conference on Information Sciences and Systems, CISS 2013

Other

Other2013 47th Annual Conference on Information Sciences and Systems, CISS 2013
CountryUnited States
CityBaltimore, MD
Period3/20/133/22/13

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