Learning to Decode Cognitive States from Brain Images
Machine Learning
Patterns of Activity in the Categorical Representations of Objects
Journal of Cognitive Neuroscience
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Regression: A Unified Approach for Sparse Subspace Learning
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Generalized sparse regularization with application to fMRI brain decoding
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Distinguishing cognitive states using iterative classification
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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The high dimensionality of functional magnetic resonance imaging (fMRI) data presents major challenges to fMRI pattern classification. Directly applying standard classifiers often results in overfitting, which limits the generalizability of the results. In this paper, we propose a new group of classifiers, "Generalized Sparse Classifiers" (GSC), to alleviate this overfitting problem. GSC draws upon the recognition that numerous standard classifiers can be reformulated under a regression framework, which enables state-of-the-art regularization techniques, e.g. elastic net, to be directly employed. Building on this regularized regression framework, we exploit an extension of elastic net that permits general properties, such as spatial smoothness, to be integrated. GSC thus facilitates simultaneous sparse feature selection and classification, while providing greater flexibility in the choice of penalties. We validate on real fMRI data and demonstrate how explicitly modeling spatial correlations inherent in brain activity using GSC can provide superior predictive performance and interpretability over standard classifiers.