Sparse optimization in feature selection: application in neuroimaging
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W. Art Chaovalitwongse | Chun-An Chou | Thomas J. Grabowski | Sonya H. Mehta | Kittipat Kampa | T. Grabowski | W. Chaovalitwongse | S. Mehta | C. Chou | K. Kampa | Kittipat Kampa
[1] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[2] Stefan Pollmann,et al. PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data , 2009, Neuroinformatics.
[3] Anders M. Dale,et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.
[4] Young-Koo Lee,et al. An Improved Maximum Relevance and Minimum Redundancy Feature Selection Algorithm Based on Normalized Mutual Information , 2010, 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet.
[5] Andreas Krause,et al. Near-optimal Nonmyopic Value of Information in Graphical Models , 2005, UAI.
[6] Thomas E. Nichols,et al. Handbook of Functional MRI Data Analysis: Index , 2011 .
[7] Edoardo Amaldi,et al. On the Approximability of Minimizing Nonzero Variables or Unsatisfied Relations in Linear Systems , 1998, Theor. Comput. Sci..
[8] Andrew W. Moore,et al. Logistic regression for data mining and high-dimensional classification , 2004 .
[9] Pedro Larrañaga,et al. A review of feature selection techniques in bioinformatics , 2007, Bioinform..
[10] Deng Cai,et al. Laplacian Score for Feature Selection , 2005, NIPS.
[11] Marko Robnik-Sikonja,et al. Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.
[12] A. E. Hoerl,et al. Ridge Regression: Applications to Nonorthogonal Problems , 1970 .
[13] Fuhui Long,et al. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[14] Le Song,et al. Supervised feature selection via dependence estimation , 2007, ICML '07.
[15] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[16] Juha Reunanen,et al. Overfitting in Making Comparisons Between Variable Selection Methods , 2003, J. Mach. Learn. Res..
[17] Ashutosh Kumar Singh,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .
[18] A. E. Hoerl,et al. Ridge regression: biased estimation for nonorthogonal problems , 2000 .
[19] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[20] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[21] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.
[22] A. Ishai,et al. Distinct, overlapping representations of faces and multiple categories of objects in ventral temporal cortex , 2001, NeuroImage.
[23] Huan Liu,et al. Semi-supervised Feature Selection via Spectral Analysis , 2007, SDM.
[24] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[25] Russell A. Poldrack,et al. Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses , 2012, NeuroImage.
[26] Sanjay Ghemawat,et al. MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.
[27] Stephen P. Boyd,et al. An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression , 2007, J. Mach. Learn. Res..
[28] Stephen José Hanson,et al. Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area? , 2004, NeuroImage.
[29] W. Art Chaovalitwongse,et al. Information-Theoretic Based Feature Selection for Multi-Voxel Pattern Analysis of fMRI Data , 2012, Brain Informatics.
[30] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[31] Stephen M. Smith,et al. Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data , 2001, NeuroImage.
[32] Bernhard Schölkopf,et al. Use of the Zero-Norm with Linear Models and Kernel Methods , 2003, J. Mach. Learn. Res..
[33] Huan Liu,et al. Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution , 2003, ICML.
[34] Tong Zhang,et al. Solving large scale linear prediction problems using stochastic gradient descent algorithms , 2004, ICML.
[35] Huan Liu,et al. Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.
[36] Lipo Wang,et al. A Modified T-test Feature Selection Method and Its Application on the HapMap Genotype Data , 2008, Genom. Proteom. Bioinform..
[37] Tom Michael Mitchell,et al. Predicting Human Brain Activity Associated with the Meanings of Nouns , 2008, Science.
[38] Huan Liu,et al. Advancing Feature Selection Research − ASU Feature Selection Repository , 2010 .
[39] C. Guestrin,et al. Near-optimal sensor placements: maximizing information while minimizing communication cost , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.
[40] Sean M. Polyn,et al. Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.
[41] Alice J. O'Toole,et al. Partially Distributed Representations of Objects and Faces in Ventral Temporal Cortex , 2005, Journal of Cognitive Neuroscience.
[42] Sayan Mukherjee,et al. Feature Selection for SVMs , 2000, NIPS.
[43] E. Mugnaini,et al. Cell junctions and intramembrane particles of astrocytes and oligodendrocytes: A freeze-fracture study , 1982, Neuroscience.
[44] Nikolaus Kriegeskorte,et al. Comparison of multivariate classifiers and response normalizations for pattern-information fMRI , 2010, NeuroImage.
[45] R. Tibshirani,et al. Least angle regression , 2004, math/0406456.
[46] J. Haynes. Brain Reading: Decoding Mental States From Brain Activity In Humans , 2011 .
[47] H. Zou,et al. Addendum: Regularization and variable selection via the elastic net , 2005 .
[48] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[49] O. Mangasarian. Minimum-support solutions of polyhedral concave programs * , 1999 .
[50] Zenglin Xu,et al. Discriminative Semi-Supervised Feature Selection Via Manifold Regularization , 2009, IEEE Transactions on Neural Networks.
[51] Shiliang Zhang,et al. Correlation-Based Feature Selection and Regression , 2010, PCM.
[52] Jean-Jacques Fuchs,et al. On the application of the global matched filter to DOA estimation with uniform circular arrays , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).
[53] Yann LeCun,et al. Large Scale Online Learning , 2003, NIPS.
[54] A. Ishai,et al. Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.
[55] R. Tibshirani,et al. Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[56] W. Art Chaovalitwongse,et al. Voxel Selection Framework in Multi-Voxel Pattern Analysis of fMRI Data for Prediction of Neural Response to Visual Stimuli , 2014, IEEE Transactions on Medical Imaging.
[57] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[58] Thomas Hofmann,et al. Map-Reduce for Machine Learning on Multicore , 2007 .
[59] Huan Liu,et al. Advancing feature selection research , 2010 .
[60] Sharon L. Thompson-Schill,et al. The advantage of brief fMRI acquisition runs for multi-voxel pattern detection across runs , 2012, NeuroImage.
[61] Michel Verleysen,et al. Advances in Feature Selection with Mutual Information , 2009, Similarity-Based Clustering.
[62] László Lovász,et al. Submodular functions and convexity , 1982, ISMP.
[63] Tom M. Mitchell,et al. Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.
[64] Carla E. Brodley,et al. Feature Selection for Unsupervised Learning , 2004, J. Mach. Learn. Res..