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Principal Feature Analysis : A Multivariate Feature Selection Method for fMRI Data
Joint Authors
Tong, Li
Zeng, Ying
Lei, Yu
Wang, Lijun
Yan, Bin
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-09-21
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Brain decoding with functional magnetic resonance imaging (fMRI) requires analysis of complex, multivariate data.
Multivoxel pattern analysis (MVPA) has been widely used in recent years.
MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods.
Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier.
Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise) or multivariate statistics.
However, these methods either discard some informative features or select features with redundant information.
This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing.
This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.
American Psychological Association (APA)
Wang, Lijun& Lei, Yu& Zeng, Ying& Tong, Li& Yan, Bin. 2013. Principal Feature Analysis : A Multivariate Feature Selection Method for fMRI Data. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-487821
Modern Language Association (MLA)
Wang, Lijun…[et al.]. Principal Feature Analysis : A Multivariate Feature Selection Method for fMRI Data. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-487821
American Medical Association (AMA)
Wang, Lijun& Lei, Yu& Zeng, Ying& Tong, Li& Yan, Bin. Principal Feature Analysis : A Multivariate Feature Selection Method for fMRI Data. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-487821
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-487821