NDLI: Spatial SVM for feature selection and fMRI activation detection

Content Provider	IEEE Xplore Digital Library
Author	Lichen Liang Cherkassky, V. Rottenberg, D.A.
Copyright Year	2006
Description	Author affiliation: Minnesota Univ., Minneapolis (Lichen Liang; Cherkassky, V.)
Abstract	This paper describes application of support vector machines (SVM) methodology for fMRI activation detection. Whereas SVM methods have been successfully used for standard predictive learning settings (i.e., classification and regression), the goal of activation detection, strictly speaking, is not achieving improved prediction accuracy. We relate the problem of activation detection in fMRI to the problem feature selection in machine learning, and describe various multivariate supervised-learning formulations for this application. Due to extreme ill-posedness of typical fMRI data sets, the quality of activation detection will be greatly affected by (a) incorporating a priori knowledge into SVM formulations, and (b) using proper encoding for training data. We analyze these issues separately, and introduce (a) novel spatial SVM formulation (reflecting a priori knowledge about local spatial correlations in fMRI data) and (b) two new encoding schemes for fMRI data that incorporate the effects of the brain dynamics (i.e., its hemodynamic response function, or HRF). The effectiveness of these modifications is clearly demonstrated using benchmark simulated and real-life fMRI data sets.
Starting Page	1463
Ending Page	1469
File Size	462346
Page Count	7
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.246867
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Data analysis Support vector machine classification Accuracy Predictive models Performance analysis Independent component analysis Supervised learning Machine learning Encoding
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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