The use of isometric transformations and bayesian estimation in compressive sensing for FMRI classification

Avishy Carmi, Tara N. Sainath, Pini Gurfil, Dimitri Kanevsky, David Nahamoo, Bhuvana Ramabhadran

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Compressive sensing (CS) is a popular technique used to reconstruct a signal from few training examples, a problem which arises in many machine learning applications. In this paper, we introduce a technique to guarantee that our data obeys certain isometric properties. In addition, we introduce a bayesian approach to compressive sensing, which we call ABCS, allowing us to obtain complete statistics for estimated parameters. We apply these ideas to fMRI classification and find that by isometrically transforming our data, significant improvements in classification accuracy can be achieved using the LASSO and Dantzig selector methods, two standard techniques used in CS. In addition, applying the ABCS method offers improvements in classification accuracy over both LASSO and Dantzig. Finally, we find that applying both the ABCS method together with isometric transformations, we are able to achieve an error rate of 0.0%.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages493-496
Number of pages4
DOIs
StatePublished - 8 Nov 2010
Externally publishedYes
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: 14 Mar 201019 Mar 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Country/TerritoryUnited States
CityDallas, TX
Period14/03/1019/03/10

Keywords

  • Bayesian learning
  • Compressive sensing
  • Image classification
  • Sparse representation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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