Algorithms for clustering data
Algorithms for clustering data
The nature of statistical learning theory
The nature of statistical learning theory
Support vector clustering for brain activation detection
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Brain Activation Detection by Neighborhood One-Class SVM
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Brain activation detection by neighborhood one-class SVM
Cognitive Systems Research
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As an exploratory approach, the clustering of fMRI time series has proved its effectiveness in analyzing the functional MRI, especially in the detection of activated regions. Due to the arbitrary distribution of fMRI time series in the temporal domain, imposing simple assumption on the data structure usually could be misleading and limit the detector's performance. Therefore, a true data-driven clustering algorithm that adapts to the data structure is preferred, and only high-level control over the clustering procedure is desired. Support vector clustering (SVC) is a suitable one in some extent because of its advantages, such as no cluster shape restriction, no need to explicitly specify the number of clusters, and the mechanism in outlier elimination. In this work, we propose an extension of the SVC to step further toward a data-sensitive detector. This approach is named as ellipsoidal support vector clustering (ESVC). To be robust to noise, the clustering is performed on features extracted from the fMRI time series via Fourier transform. Experimental results on simulated and real data sets demonstrate the effectiveness of incorporating data structure in clustering fMRI time series.