NDLI: About analysis and robust classification of searchlight fMRI-data using machine learning classifiers

Content Provider	IEEE Xplore Digital Library
Author	Lange, M. Kastner, M. Villmann, T.
Copyright Year	2013
Description	Author affiliation: Comput. Intell. Group at the Dept. for Math./Natural & Comput. Sci., Univ. of Appl. Sci. Mittweida, Mittweida, Germany (Lange, M.; Kastner, M.; Villmann, T.)
Abstract	In the present paper we investigate the analysis of functional magnetic resonance image (fMRI) data based on voxel response analysis. All voxels in local spatial area (volume) of a considered voxel form its so-called searchlight. The searchlight for a presented task is taken as a complex pattern. Task dependent discriminant analysis of voxel is then performed by assessment of the discrimination behavior of the respective searchlight pattern for a given task. Classification analysis of these patterns is usually done using linear support vector machines (linSVMs) as a machine learning approach or another statistical classifier like linear discriminant classifier. The test classification accuracy determining the task sensitivity is interpreted as the discrimination ability of the related voxel. However, frequently, the number of voxels contributing to a searchlight is much larger than the number of available pattern samples in classification learning, i.e. the dimensionality of patterns is higher than the number of samples. Therefore, the respective underlying mathematical classification problem has not an unique solution such that a certain solution obtained by the machine learning classifier contains arbitrary (random) components. For this situation, the generalization ability of the classifier may drop down. We propose in this paper another data processing approach to reduce this problem. In particular, we reformulate the classification problem within the searchlight. Doing so, we avoid the dimensionality problem: We obtain a mathematically well-defined classification problem, such that generalization ability of a trained classifier is kept high. Hence, a better stability of the task discrimination is obtained. Additionally, we propose the utilization of generalized learning vector quantizers as an alternative machine learning classifier system compared to SVMs, to improve further the stability of the classifier model due to decreased model complexity.
Starting Page	1
Ending Page	8
File Size	352511
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781467361293
DOI	10.1109/IJCNN.2013.6706990
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Vectors Training Support vector machines Data models Standards Complexity theory
Content Type	Text
Resource Type	Article

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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
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