A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Model-free functional MRI analysis based on unsupervised clustering
Journal of Biomedical Informatics
Learning to Decode Cognitive States from Brain Images
Machine Learning
Partially Distributed Representations of Objects and Faces in Ventral Temporal Cortex
Journal of Cognitive Neuroscience
Patterns of Activity in the Categorical Representations of Objects
Journal of Cognitive Neuroscience
A correlation approach for automatic image annotation
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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We introduce a new unsupervised fMRI analysis method based on Kernel Canonical Correlation Analysis which differs from the class of supervised learning methods that are increasingly being employed in fMRI data analysis. Whereas SVM associates properties of the imaging data with simple specific categorical labels, KCCA replaces these simple labels with a label vector for each stimulus containing details of the features of that stimulus. We have compared KCCA and SVM analyses of an fMRI data set involving responses to emotionally salient stimuli. This involved first training the algorithm ( SVM, KCCA) on a subset of fMRI data and the corresponding labels/label vectors, then testing the algorithms on data withheld from the original training phase. The classification accuracies of SVM and KCCA proved to be very similar. However, the most important result arising from this study is that KCCA in able in part to extract many of the brain regions that SVM identifies as the most important in task discrimination blind to the categorical task labels.