Generalized Sparse Regularization with Application to fMRI Brain Decoding
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[1] Cees G. M. Snoek,et al. Variable Selection , 2019, Model-Based Clustering and Classification for Data Science.
[2] Ghassan Hamarneh,et al. Generalized Sparse Classifiers for Decoding Cognitive States in fMRI , 2010, MLMI.
[3] Bertrand Thirion,et al. Multi-Class Sparse Bayesian Regression for Neuroimaging Data Analysis , 2010, MLMI.
[4] Yongyi Yang,et al. Machine Learning in Medical Imaging , 2010, IEEE Signal Processing Magazine.
[5] Kaustubh Supekar,et al. Sparse logistic regression for whole-brain classification of fMRI data , 2010, NeuroImage.
[6] R. Tibshirani,et al. The solution path of the generalized lasso , 2010, 1005.1971.
[7] Yonina C. Eldar,et al. Collaborative hierarchical sparse modeling , 2010, 2010 44th Annual Conference on Information Sciences and Systems (CISS).
[8] T. Heskes,et al. Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior , 2010, NeuroImage.
[9] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[10] R. Tibshirani,et al. A note on the group lasso and a sparse group lasso , 2010, 1001.0736.
[11] Jürgen Hennig,et al. Fully automated classification of HARDI in vivo data using a support vector machine , 2009, NeuroImage.
[12] Jean-Philippe Vert,et al. Group lasso with overlap and graph lasso , 2009, ICML '09.
[13] A. Ravishankar Rao,et al. Prediction and interpretation of distributed neural activity with sparse models , 2009, NeuroImage.
[14] Michael P. Friedlander,et al. Probing the Pareto Frontier for Basis Pursuit Solutions , 2008, SIAM J. Sci. Comput..
[15] Masa-aki Sato,et al. Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns , 2008, NeuroImage.
[16] Tom Michael Mitchell,et al. Predicting Human Brain Activity Associated with the Meanings of Nouns , 2008, Science.
[17] P. Rossini,et al. The role of the prefrontal cortex in sentence comprehension: An rTMS study , 2008, Cortex.
[18] Jiawei Han,et al. Spectral Regression: A Unified Approach for Sparse Subspace Learning , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).
[19] Jean-Baptiste Poline,et al. Dealing with the shortcomings of spatial normalization: Multi‐subject parcellation of fMRI datasets , 2006, Human brain mapping.
[20] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[21] R. Tibshirani,et al. Sparsity and smoothness via the fused lasso , 2005 .
[22] Tom M. Mitchell,et al. Learning to Decode Cognitive States from Brain Images , 2004, Machine Learning.
[23] J. -B. Poline,et al. Estimating the Delay of the fMRI Response , 2002, NeuroImage.
[24] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[25] Richard S. J. Frackowiak,et al. Functional anatomy of a common semantic system for words and pictures , 1996, Nature.
[26] Stephen Lin,et al. Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[28] J. Fodor. The Modularity of mind. An essay on faculty psychology , 1986 .
[29] © Institute of Mathematical Statistics, 2004 LEAST ANGLE REGRESSION , 2022 .