TY - GEN
T1 - The use of isometric transformations and bayesian estimation in compressive sensing for FMRI classification
AU - Carmi, Avishy
AU - Sainath, Tara N.
AU - Gurfil, Pini
AU - Kanevsky, Dimitri
AU - Nahamoo, David
AU - Ramabhadran, Bhuvana
PY - 2010/1/1
Y1 - 2010/1/1
N2 - 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%.
AB - 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%.
KW - Bayesian learning
KW - Compressive sensing
KW - Image classification
KW - Sparse representation
UR - http://www.scopus.com/inward/record.url?scp=78049382160&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2010.5495673
DO - 10.1109/ICASSP.2010.5495673
M3 - Conference contribution
AN - SCOPUS:78049382160
SN - 9781424442966
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 493
EP - 496
BT - 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
PB - Institute of Electrical and Electronics Engineers
T2 - 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Y2 - 14 March 2010 through 19 March 2010
ER -