Peculiarity Oriented Multidatabase Mining
IEEE Transactions on Knowledge and Data Engineering
The Journal of Machine Learning Research
Support Vector Data Description
Machine Learning
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Ellipsoidal support vector clustering for functional MRI analysis
Pattern Recognition
Peculiarity oriented fMRI brain data analysis for studying human multi-perception mechanism
Cognitive Systems Research
Clustering of fMRI data using affinity propagation
BI'10 Proceedings of the 2010 international conference on Brain informatics
Exploring functional connectivity networks in fMRI data using clustering analysis
BI'11 Proceedings of the 2011 international conference on Brain informatics
Exploring brain activation patterns during heuristic problem solving using clustering approach
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Brain activation detection by neighborhood one-class SVM
Cognitive Systems Research
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Brain activation detection is an important problem in fMRI data analysis. In this paper, we propose a data-driven activation detection method called neighborhood one-class SVM (NOC-SVM). By incorporating the idea of neighborhood consistency into one-class SVM, the method classifies a voxel as an activated or non-activated voxel by its neighbor weighted distance to a hyperplane in a high-dimensional kernel space. On two synthetic datasets under different SNRs, the proposed method almost has lower error rate than K-means clustering and fuzzy K-means clustering. On a real fMRI dataset, all the three algorithms can detect similar activated regions. Furthermore, the NOC-SVM is more stable than random algorithms, such as K-means clustering and fuzzy K-means clustering. These results show that the proposed NOC-SVM is a new effective method for activation detections in fMRI data.