Sparse methods for biomedical data
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Jieping Ye | Jun Liu | Jieping Ye | Jun Liu
[1] Peter Kellman,et al. Real‐time accelerated interactive MRI with adaptive TSENSE and UNFOLD , 2003, Magnetic resonance in medicine.
[2] M. Wainwright,et al. Joint support recovery under high-dimensional scaling: Benefits and perils of ℓ 1,∞ -regularization , 2008, NIPS 2008.
[3] Tom Goldstein,et al. The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..
[4] Yurii Nesterov,et al. Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.
[5] L. Ying,et al. Accelerating SENSE using compressed sensing , 2009, Magnetic resonance in medicine.
[6] A. C. Brau,et al. ESPIRiT ( Efficient Eigenvector-Based L 1 SPIRiT ) for Compressed Sensing Parallel Imaging-Theoretical Interpretation and Improved Robustness for Overlapped FOV Prescription , 2010 .
[7] Joel A. Tropp,et al. Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit , 2006, Signal Process..
[8] Y. Nesterov. Gradient methods for minimizing composite objective function , 2007 .
[9] Mikhail Belkin,et al. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.
[10] Jianqing Fan,et al. A Selective Overview of Variable Selection in High Dimensional Feature Space. , 2009, Statistica Sinica.
[11] M. Kowalski. Sparse regression using mixed norms , 2009 .
[12] Xiaotong Shen,et al. Simultaneous Grouping Pursuit and Feature Selection Over an Undirected Graph , 2013, Journal of the American Statistical Association.
[13] Le Song,et al. Estimating time-varying networks , 2008, ISMB 2008.
[14] Stephen P. Boyd,et al. An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression , 2007, J. Mach. Learn. Res..
[15] Alexandre d'Aspremont,et al. Model Selection Through Sparse Max Likelihood Estimation Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data , 2022 .
[16] J. Friedman,et al. New Insights and Faster Computations for the Graphical Lasso , 2011 .
[17] Peter Kellman. Parallel Imaging : The Basics , 2002 .
[18] Wei Chu,et al. Biomarker discovery in microarray gene expression data with Gaussian processes , 2005, Bioinform..
[19] Jieping Ye,et al. Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization , 2009, UAI.
[20] Edgar Mueller,et al. Dynamic cardiac MRI reconstruction with weighted redundant Haar wavelets , 2022 .
[21] José M. Bioucas-Dias,et al. An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems , 2009, IEEE Transactions on Image Processing.
[22] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[23] Massimiliano Pontil,et al. Convex multi-task feature learning , 2008, Machine Learning.
[24] Xiaotong Shen,et al. Journal of the American Statistical Association Likelihood-based Selection and Sharp Parameter Estimation Likelihood-based Selection and Sharp Parameter Estimation , 2022 .
[25] Francis R. Bach,et al. Structured Variable Selection with Sparsity-Inducing Norms , 2009, J. Mach. Learn. Res..
[26] H. Bondell,et al. Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR , 2008, Biometrics.
[27] S. Panchanathan,et al. BEST: a novel computational approach for comparing gene expression patterns from early stages of Drosophila melanogaster development. , 2002, Genetics.
[28] P. Brown,et al. Drug target validation and identification of secondary drug target effects using DNA microarrays , 1998, Nature Medicine.
[29] Robin M Heidemann,et al. SMASH, SENSE, PILS, GRAPPA: How to Choose the Optimal Method , 2004, Topics in magnetic resonance imaging : TMRI.
[30] R. Tibshirani,et al. Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.
[31] Stephen P. Boyd,et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..
[32] Trevor J. Hastie,et al. The Graphical Lasso: New Insights and Alternatives , 2011, Electronic journal of statistics.
[33] E.J. Candes,et al. An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.
[34] G. Obozinski. Joint covariate selection for grouped classification , 2007 .
[35] Tso-Jung Yen,et al. Discussion on "Stability Selection" by Meinshausen and Buhlmann , 2010 .
[36] Leslie Greengard,et al. Accelerating the Nonuniform Fast Fourier Transform , 2004, SIAM Rev..
[37] M. Lustig,et al. SPIRiT: Iterative self‐consistent parallel imaging reconstruction from arbitrary k‐space , 2010, Magnetic resonance in medicine.
[38] Cun-Hui Zhang,et al. Confidence Intervals for Low-Dimensional Parameters With High-Dimensional Data , 2011 .
[39] Richard G. Baraniuk,et al. Compressive Sensing , 2008, Computer Vision, A Reference Guide.
[40] Jun Liu,et al. Regularized reconstruction using redundant Haar wavelets : A means to achieve high under-sampling factors in non-contrast-enhanced 4 D MRA , .
[41] R. Tibshirani,et al. A note on the group lasso and a sparse group lasso , 2010, 1001.0736.
[42] Fan Chung,et al. Spectral Graph Theory , 1996 .
[43] R. Tibshirani,et al. �-norm Support Vector Machines , 2003 .
[44] Peter Bühlmann,et al. Missing values: sparse inverse covariance estimation and an extension to sparse regression , 2009, Statistics and Computing.
[45] Julien Mairal,et al. Proximal Methods for Sparse Hierarchical Dictionary Learning , 2010, ICML.
[46] Robin M Heidemann,et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA) , 2002, Magnetic resonance in medicine.
[47] Eric P. Xing,et al. Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity , 2009, ICML.
[48] H. Zou,et al. Structured variable selection and estimation , 2009, 1011.0610.
[49] T. Ideker,et al. Network-based classification of breast cancer metastasis , 2007, Molecular systems biology.
[50] R. Tibshirani,et al. PATHWISE COORDINATE OPTIMIZATION , 2007, 0708.1485.
[51] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.
[52] Christof Baltes,et al. k‐t BLAST reconstruction from non‐Cartesian k‐t space sampling , 2006, Magnetic resonance in medicine.
[53] Scott E. Fraser,et al. Imaging in Systems Biology , 2007, Cell.
[54] Trevor Darrell,et al. An efficient projection for l1, ∞ regularization , 2009, ICML '09.
[55] Jun Liu,et al. Efficient `1=`q Norm Regularization , 2010 .
[56] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[57] Kaustubh Supekar,et al. Sparse logistic regression for whole-brain classification of fMRI data , 2010, NeuroImage.
[58] W. Manning,et al. Simultaneous acquisition of spatial harmonics (SMASH): Fast imaging with radiofrequency coil arrays , 1997, Magnetic resonance in medicine.
[59] T. Hromádka,et al. Sparse Representation of Sounds in the Unanesthetized Auditory Cortex , 2008, PLoS biology.
[60] Kedar Khare,et al. Accelerated MR imaging using compressive sensing with no free parameters , 2012, Magnetic resonance in medicine.
[61] Peter Buhlmann. Statistical significance in high-dimensional linear models , 2012, 1202.1377.
[62] W. McGinnis,et al. From DNA to Diversity, Molecular Genetics and the Evolution of Animal Design, 2nd edition , 2005 .
[63] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[64] M. Ashburner,et al. Systematic determination of patterns of gene expression during Drosophila embryogenesis , 2002, Genome Biology.
[65] Larry A. Wasserman,et al. Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models , 2010, NIPS.
[66] Pablo Tamayo,et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[67] Jieping Ye,et al. Moreau-Yosida Regularization for Grouped Tree Structure Learning , 2010, NIPS.
[68] N. Gostling,et al. From DNA to Diversity: Molecular Genetics and the Evolution of Animal Design , 2002, Heredity.
[69] Yu-Chung N. Cheng,et al. Magnetic Resonance Imaging: Physical Principles and Sequence Design , 1999 .
[70] Ji Zhu,et al. Regularized Multivariate Regression for Identifying Master Predictors with Application to Integrative Genomics Study of Breast Cancer. , 2008, The annals of applied statistics.
[71] Eric P. Xing,et al. On Time Varying Undirected Graphs , 2011, AISTATS.
[72] Jeffrey A. Fessler,et al. Parallel MR Image Reconstruction Using Augmented Lagrangian Methods , 2011, IEEE Transactions on Medical Imaging.
[73] M Usman,et al. k‐t group sparse: A method for accelerating dynamic MRI , 2011, Magnetic resonance in medicine.
[74] Emmanuel J. Candès,et al. Quantitative Robust Uncertainty Principles and Optimally Sparse Decompositions , 2004, Found. Comput. Math..
[75] M. Elad,et al. An EigenVector Approach to AutoCalibrating Parallel MRI , Where SENSE Meets GRAPPA , 2010 .
[76] R. Tibshirani,et al. Sparse Principal Component Analysis , 2006 .
[77] Jieping Ye,et al. Feature grouping and selection over an undirected graph , 2012, KDD.
[78] T. Hastie,et al. Classification of gene microarrays by penalized logistic regression. , 2004, Biostatistics.
[79] P. Bühlmann,et al. The group lasso for logistic regression , 2008 .
[80] Tong Zhang,et al. Analysis of Multi-stage Convex Relaxation for Sparse Regularization , 2010, J. Mach. Learn. Res..
[81] D. Donoho,et al. Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.
[82] Junzhou Huang,et al. Learning with structured sparsity , 2009, ICML '09.
[83] R.G. Baraniuk,et al. Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.
[84] Wotao Yin,et al. A Fast Hybrid Algorithm for Large-Scale l1-Regularized Logistic Regression , 2010, J. Mach. Learn. Res..
[85] Hongzhe Li,et al. In Response to Comment on "Network-constrained regularization and variable selection for analysis of genomic data" , 2008, Bioinform..
[86] Patrick Danaher,et al. The joint graphical lasso for inverse covariance estimation across multiple classes , 2011, Journal of the Royal Statistical Society. Series B, Statistical methodology.
[87] Jing Li,et al. Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data , 2009, NIPS.
[88] Grace Wahba,et al. Detecting disease-causing genes by LASSO-Patternsearch algorithm , 2007, BMC proceedings.
[89] Jieping Ye,et al. Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval , 2012, BMC Bioinformatics.
[90] F H Epstein,et al. Adaptive sensitivity encoding incorporating temporal filtering (TSENSE) † , 2001, Magnetic resonance in medicine.
[91] D. Larkman,et al. Parallel magnetic resonance imaging , 2007, Physics in medicine and biology.
[92] Trevor J. Hastie,et al. Genome-wide association analysis by lasso penalized logistic regression , 2009, Bioinform..
[93] Han Liu,et al. Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery , 2009, ICML '09.
[94] Michael I. Jordan,et al. A Direct Formulation for Sparse Pca Using Semidefinite Programming , 2004, NIPS 2004.
[95] Jean-Philippe Vert,et al. Group lasso with overlap and graph lasso , 2009, ICML '09.
[96] Trevor J. Hastie,et al. Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso , 2011, J. Mach. Learn. Res..
[97] P. Zhao,et al. The composite absolute penalties family for grouped and hierarchical variable selection , 2009, 0909.0411.
[98] R. Tibshirani,et al. Spatial smoothing and hot spot detection for CGH data using the fused lasso. , 2008, Biostatistics.
[99] A. Ng. Feature selection, L1 vs. L2 regularization, and rotational invariance , 2004, Twenty-first international conference on Machine learning - ICML '04.
[100] Richard G. Baraniuk,et al. Sparse Signal Detection from Incoherent Projections , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[101] Robert D. Nowak,et al. Compressive wireless sensing , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.
[102] Gregory Piatetsky-Shapiro,et al. High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality , 2000 .
[103] N. Meinshausen,et al. Stability selection , 2008, 0809.2932.
[104] Robert Tibshirani,et al. 1-norm Support Vector Machines , 2003, NIPS.
[105] Yunmei Chen,et al. Computational Acceleration for MR Image Reconstruction in Partially Parallel Imaging , 2011, IEEE Transactions on Medical Imaging.
[106] Leon Wenliang Zhong,et al. Efficient Sparse Modeling With Automatic Feature Grouping , 2011, IEEE Transactions on Neural Networks and Learning Systems.
[107] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[108] Hanchuan Peng,et al. Bioimage informatics: a new area of engineering biology , 2008, Bioinform..
[109] Jieping Ye,et al. Efficient Sparse Group Feature Selection via Nonconvex Optimization , 2012, ICML.
[110] E. Xing,et al. Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network , 2009, PLoS genetics.
[111] P. Boesiger,et al. SENSE: Sensitivity encoding for fast MRI , 1999, Magnetic resonance in medicine.
[112] Shuiwang Ji,et al. SLEP: Sparse Learning with Efficient Projections , 2011 .
[113] E. Levina,et al. Joint estimation of multiple graphical models. , 2011, Biometrika.
[114] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[115] David L Donoho,et al. Compressed sensing , 2006, IEEE Transactions on Information Theory.
[116] T. Hohage,et al. Image reconstruction by regularized nonlinear inversion—Joint estimation of coil sensitivities and image content , 2008, Magnetic resonance in medicine.
[117] Francis R. Bach,et al. Consistency of the group Lasso and multiple kernel learning , 2007, J. Mach. Learn. Res..
[118] Leslie Ying,et al. Joint image reconstruction and sensitivity estimation in SENSE (JSENSE) , 2007, Magnetic resonance in medicine.
[119] Arkadi Nemirovski,et al. EFFICIENT METHODS IN CONVEX PROGRAMMING , 2007 .
[120] Trevor Darrell,et al. An efficient projection for l 1 , infinity regularization. , 2009, ICML 2009.
[121] Po-Ling Loh,et al. High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity , 2011, NIPS.
[122] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[123] Paul M. Thompson,et al. Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data , 2012, NeuroImage.
[124] Jianmin Wang,et al. Field‐of‐view limitations in parallel imaging , 2004, Magnetic resonance in medicine.
[125] D.,et al. Regression Models and Life-Tables , 2022 .
[126] R. Tibshirani,et al. Sparsity and smoothness via the fused lasso , 2005 .
[127] Jieping Ye,et al. Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data , 2012, BMC Neurology.