Linked Life Data

20482215

Source:http://linkedlifedata.com/resource/pubmed/id/20482215

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
18
pubmed:dateCreated
2010-5-20
pubmed:abstractText
Compressed sensing (CS) is an important recent advance that shows how to reconstruct sparse high dimensional signals from surprisingly small numbers of random measurements. The nonlinear nature of the reconstruction process poses a challenge to understanding the performance of CS. We employ techniques from the statistical physics of disordered systems to compute the typical behavior of CS as a function of the signal sparsity and measurement density. We find surprising and useful regularities in the nature of errors made by CS, a new phase transition which reveals the possibility of CS for nonnegative signals without optimization, and a new null model for sparse regression.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1079-7114
pubmed:author
pubmed:issnType
Electronic
pubmed:day
7
pubmed:volume
104
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
188701
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Statistical mechanics of compressed sensing.
pubmed:affiliation
Sloan Swartz Center for Theoretical Neurobiology, UCSF, San Francsico, California 94143, USA. surya@phy.ucsf.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't