A Sparse-Group Lasso

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625

Regression shrinkage and selection via the lasso

Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482

Regularization and Variable Selection Via the Elastic Net

Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957

Regularization Paths for Generalized Linear Models via Coordinate Descent

Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927

Model Selection and Estimation in Regression with Grouped Variables

Yuan, M., & Lin, Y. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00532.x · Cited by 7,261

The Group Lasso for Logistic Regression

Meier, L., van de Geer, S., & Bühlmann, P. (2008) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00627.x · Cited by 1,681

A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen

Ma, X.-J., Wang, Z., Ryan, P. D., Isakoff, S. J., Barmettler, A., Fuller, A. P., Muir, B., Mohapatra, G., Salunga, R. C., Tuggle, J. T., et al. (2004) · Cancer Cell · doi:10.1016/j.ccr.2004.05.015 · Cited by 959

NESTA: A Fast and Accurate First-Order Method for Sparse Recovery

Becker, S., Bobin, J., & Candès, E. J. (2009) · Cited by 897

Gradient methods for minimizing composite objective function

Nesterov, Y. E. (2007) · Cited by 867

Group lasso with overlap and graph lasso

Jacob, L., Obozinski, G., & Vert, J. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553431 · Cited by 842

Decoupled Learning for Factorial Marked Temporal Point Processes

Wu, W., Yan, J., Yang, X., & Hongyuan Zha, B. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220035 · Cited by 21

Harmonization of diffusion MRI data sets with adaptive dictionary learning

St‐Jean, S., Viergever, M. A., & Leemans, A. (2020) · Human Brain Mapping · doi:10.1002/hbm.25117 · Cited by 21

Model-based regression clustering for high-dimensional data: application to functional data

Devijver, É. (2016) · Advances in Data Analysis and Classification · doi:10.1007/s11634-016-0242-1 · Cited by 21

Structured Sparse Regression via Greedy Hard-ThresholdingPreprint

Jain, P., Rao, N., & Dhillon, I. S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.06042 · Cited by 21

PIntMF: Penalized Integrative Matrix Factorization method for multi-omics data

Pierre-Jean, M., Mauger, F., Deleuze, J.-F., & Le Floch, E. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab786 · Cited by 20

Evolutionary sparse learning for phylogenomics

Kumar, S., & Sharma, S. (2021) · Molecular Biology and Evolution · doi:10.1093/molbev/msab227 · Cited by 22

Variable selection in linear regression models: Choosing the best subset is not always the best choice

Hanke, M., Dijkstra, L., Foraita, R., & Didelez, V. (2023) · Biometrical Journal · doi:10.1002/bimj.202200209 · Cited by 20

Convergence Analysis of Online Gradient Method for High-Order Neural Networks and Their Sparse Optimization

Fan, Q., Kang, Q. K., Żurada, J. M., Huang, T., & Xu, D. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3319989 · Cited by 20

Better diagnostic signatures from RNAseq data through use of auxiliary co-data

Novianti, P. W., Snoek, B. C., Wilting, S. M., & van de Wiel, M. A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw837 · Cited by 20

Proteomics analysis to reveal biological pathways and predictive proteins in the survival of high-grade serous ovarian cancer

Xie, H., Wang, W., Sun, F., Deng, K., Lu, X. L., Liu, H., Zhao, W., Zhang, Y., Zhou, X., Li, K., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-10559-9 · Cited by 20

Sparse Range-Constrained Learning and Its Application for Medical Image Grading

Cheng, J. T. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2851607 · Cited by 20

Asymptotic theory of the adaptive Sparse Group Lasso

Poignard, B. (2018) · Annals of the Institute of Statistical Mathematics · doi:10.1007/s10463-018-0692-7 · Cited by 20

Gsslasso Cox: a Bayesian hierarchical model for predicting survival and detecting associated genes by incorporating pathway information

Tang, Z., Lei, S., Zhang, X., Yi, Z., Guo, B., Chen, J. Y., Shen, Y., & Yi, N. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2656-1 · Cited by 20

EEG Emotion Recognition Based on Granger Causality and CapsNet Neural Network

Guo, J., Fang, F., Wang, W., & Ren, F. (2018) · IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) · doi:10.1109/ccis.2018.8691230 · Cited by 20

Neural Architecture Search as Sparse Supernet

Wu, Y. X., Liu, A., Huang, Z., Zhang, S., & Gool, L. V. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17243 · Cited by 20

A Mixed Integer Linear Programming Support Vector Machine for Cost-Effective Group Feature Selection: Branch-Cut-and-Price Approach

Lee, I. G., Yoon, S. W., & Won, D. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.12.030 · Cited by 20

A Bayesian predictive model for imaging genetics with application to schizophrenia

Chekouo, T., Stingo, F. C., Guindani, M., & Do, K.-A. (2016) · The Annals of Applied Statistics · doi:10.1214/16-aoas948 · Cited by 21

Joint Estimation of Multiple Conditional Gaussian Graphical Models

Huang, F., Chen, S., & Huang, S.-J. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2710090 · Cited by 20

Thermodynamics of solids including anharmonicity through quasiparticle theory

Blancas, E. J., Lobato, Á., Izquierdo‐Ruiz, F., Márquez, A. M., Recio, J. M., Nath, P., Plata, J. J., & Otero‐de‐la‐Roza, A. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01447-8 · Cited by 20

Gradient-based Regularization Parameter Selection for Problems With Nonsmooth Penalty Functions

Feng, J., & Simon, N. (2017) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2017.1390470 · Cited by 19

Online group streaming feature selection using entropy-based uncertainty measures for fuzzy neighborhood rough sets

Xu, J., Sun, Y., Qu, K., Meng, X., & Hou, Q. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00763-0 · Cited by 19

Sparse Overlapping Group Lasso for Integrative Multi-Omics Analysis

Park, H., Niida, A., Miyano, S., & Imoto, S. (2015) · Journal of Computational Biology · doi:10.1089/cmb.2014.0197 · Cited by 19

Leveraging hierarchy in medical codes for predictive modeling

Singh, A., Nadkarni, G. N., Guttag, J. V., & Böttinger, E. P. (2014) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2649387.2649407 · Cited by 19

Automatic node selection for Deep Neural Networks using Group Lasso regularization

Ochiai, T., Matsuda, S., Watanabe, H., & Katagiri, S. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7953205 · Cited by 19

Model Confidence Bounds for Variable Selection

Li, Y., Luo, Y., Ferrari, D., Hu, X., & Qin, Y. (2019) · Biometrics · doi:10.1111/biom.13024 · Cited by 19

Sparse Extended Redundancy Analysis: Variable Selection via the Exclusive LASSO

Kok, B. C., Choi, J. S., Oh, H., & Choi, J. Y. (2019) · Multivariate Behavioral Research · doi:10.1080/00273171.2019.1694477 · Cited by 19

Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint

Chen, Y., Koch, T., Zakiyeva, N., & Zhu, B. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115597 · Cited by 19

Structured sparse multiset canonical correlation analysis of simultaneous fNIRS and EEG provides new insights into the human action-observation network

Dashtestani, H., Miguel, H. O., Condy, E. E., Zeytinoglu, S., Millerhagen, J., Debnath, R., Smith, E. G., Adalı, T., Fox, N. A., & Gandjbakhche, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-10942-1 · Cited by 19

Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach

Yang, Y., Hu, L., Chen, Y., Gu, W., Lin, G., Xie, Y., & Nie, S. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1510192 · Cited by 18

A statistical pipeline for identifying physical features that differentiate classes of 3D shapes

Wang, B., Sudijono, T., Kirveslahti, H., Gao, T., Boyer, D., Mukherjee, S., & Crawford, L. A. (2021) · The Annals of Applied Statistics · doi:10.1214/20-aoas1430 · Cited by 18

A novel meta-analysis based on data augmentation and elastic data shared lasso regularization for gene expression

Huang, H., Rao, H., Miao, R., & Liang, Y. Q. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04887-5 · Cited by 18

Identification of immune-related biomarkers in peripheral blood of schizophrenia using bioinformatic methods and machine learning algorithms

Zhu, X., Wang, C., Yu, J., Weng, J., Han, B., Liu, Y., Tang, X., & Pan, B. (2023) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2023.1256184 · Cited by 18

Structured Sparsity: Discrete and Convex Approaches

Kyrillidis, A., Baldassarre, L., Halabi, M. E., Tran-Dinh, Q., & Cevher, V. (2015) · Applied and numerical harmonic analysis · doi:10.1007/978-3-319-16042-9_12 · Cited by 18

Tree-Structured Nuclear Norm Approximation With Applications to Robust Face Recognition

Luo, L., Chen, L., Yang, J. J., Qian, J., & Zhang, B. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2612885 · Cited by 18

Visible Light-Based User Position, Orientation and Channel Estimation Using Self-Adaptive Location-Domain Grid Sampling

Zhou, B., Liu, A., & Lau, V. K. N. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.2988907 · Cited by 18

Linear convergence of inexact descent method and inexact proximal gradient algorithms for lower-order regularization problems

Hu, Y., Li, C., Meng, K., & Yang, X. (2020) · Journal of Global Optimization · doi:10.1007/s10898-020-00955-3 · Cited by 18

Development and validation of multivariable prediction models for adverse COVID-19 outcomes in patients with IBD

Sperger, J., Shah, K. S., Lu, M., Zhang, X., Ungaro, R. C., Brenner, E. J., Agrawal, M., Colombel, J., Kappelman, M. D., & Kosorok, M. R. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-049740 · Cited by 18

The Application of Bayesian Methods in Cancer Prognosis and Prediction

Chu, J., Sun, N., Hu, W., Chen, X., Yi, N., & Shen, Y. (2021) · Cancer Genomics & Proteomics · doi:10.21873/cgp.20298 · Cited by 18

Joint selection of brain network nodes and edges for MCI identification

Jiang, X., Qiao, L., Leone, R. D., & Shen, D. G. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107082 · Cited by 18

Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision TreesPreprint

Renard, X., Woloszko, N., Aigrain, J., & Detyniecki, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01297 · Cited by 18

The Joint Cross Section of Option and Stock Returns Predictability with Big Data and Machine LearningPreprint

Goyenko, R. Y., & Zhang, C. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3747238 · Cited by 18

Is Seeing Believing? A Practitioner’s Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies

Fan, K., Subedi, S., Yang, G., Xi, L., Ren, J., & Wu, C. (2024) · Entropy · doi:10.3390/e26090794 · Cited by 17

Sparse partial least squares with group and subgroup structure

Sutton, M., Thiébaut, R., & Liquet, B. (2018) · Statistics in Medicine · doi:10.1002/sim.7821 · Cited by 17

Latent Network Structure Learning From High-Dimensional Multivariate Point Processes

Cai, B., Zhang, J., & Guan, Y. (2022) · Journal of the American Statistical Association · doi:10.1080/01621459.2022.2102019 · Cited by 17

Interpretable principal component analysis for multilevel multivariate functional data

Zhang, J., Siegle, G. J., Sun, T., D'Andrea, W. M., & Krafty, R. T. (2021) · Biostatistics · doi:10.1093/biostatistics/kxab018 · Cited by 17

Simulation-Selection-Extrapolation: Estimation in High-Dimensional Errors-in-Variables Models

Nghiem, L. H., & Potgieter, C. J. (2019) · Biometrics · doi:10.1111/biom.13112 · Cited by 17

cmenet : A New Method for Bi-Level Variable Selection of Conditional Main Effects

Mak, S., & Wu, C. F. J. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1448828 · Cited by 17

Fast Sparse Group Lasso

Ida, Y., Fujiwara, Y., & Kashima, H. (2019) · neural information processing systems · Cited by 17

Using the “Hidden” genome to improve classification of cancer types

Chakraborty, S., Begg, C. B., & Shen, R. (2020) · Biometrics · doi:10.1111/biom.13367 · Cited by 17

A Group Feature Ranking and Selection Method Based on Dimension Reduction Technique in High-Dimensional Data

Zubair, I. M., & Kim, B. (2022) · IEEE Access · doi:10.1109/access.2022.3225685 · Cited by 17

Time-domain sound field reproduction using the group Lasso

Feng, Q., Yang, F., & Yang, J. J. (2018) · The Journal of the Acoustical Society of America · doi:10.1121/1.5022280 · Cited by 17

Dual-sPLS: a family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity; evaluation on simulated and near-infrared (NIR) dataPreprint

Alsouki, L., Duval, L. C., Marteau, C., Haddad, R. E., & Wahl, F. (2023) · HAL · Cited by 17

A phase congruency based patch evaluator for complexity reduction in multi-dictionary based single-image super-resolution

Zhou, Y., Kwong, S., Gao, W., & Wang, X. (2016) · Information Sciences · doi:10.1016/j.ins.2016.05.024 · Cited by 16

Multi-omics regulatory network inference in the presence of missing data

Henao, J., Lauber, M., Azevedo, M., Grekova, A., Theis, F. J., List, M., Ogris, C., & Schubert, B. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad309 · Cited by 16

Multi-Target Location and Doppler Estimation in Multistatic Automotive Radar Applications

Moussa, A., Liu, W., Zhang, Y. D., & Greco, M. (2024) · IEEE Transactions on Radar Systems · doi:10.1109/trs.2024.3362706 · Cited by 16

Split Bregman algorithms for sparse group Lasso with application to MRI reconstruction

Zou, J. X., & Fu, Y. (2014) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-014-0282-7 · Cited by 16

Sparse Localization of Harmonic Audio Sources

Adalbjörnsson, S. I., Kronvall, T., Burgess, S., Åström, K., & Jakobsson, A. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2497798 · Cited by 16

Adaptive Class Preserving Representation for Image Classification

Mi, J., Fu, Q., & Li, W. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.281 · Cited by 16

Convergence of a Gradient-Based Learning Algorithm With Penalty for Ridge Polynomial Neural Networks

Fan, Q., Peng, J., Li, H., & Lin, S. (2020) · IEEE Access · doi:10.1109/access.2020.3048235 · Cited by 16

Structured Sparsity of Convolutional Neural Networks via Nonconvex Sparse Group Regularization

Bui, K., Park, F., Zhang, S., Qi, Y., & Xin, J. X. (2021) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2020.529564 · Cited by 17

Learning Sparse Neural Networks Using Non-Convex Regularization

Pandit, M. K., Naaz, R., & Chishti, M. A. (2021) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2021.3058672 · Cited by 16

On sparse high-dimensional graphical model learning for dependent time series

TUGNAIT, J. K. (2022) · Signal Processing · doi:10.1016/j.sigpro.2022.108539 · Cited by 16

DeepDNAbP: A deep learning-based hybrid approach to improve the identification of deoxyribonucleic acid-binding proteins

Hosen, M. F., Mahmud, S., Ahmed, K., Chen, W., Moni, M. A., Deng, H., Shoombuatong, W., & Hasan, M. M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105433 · Cited by 16

An algorithm for the multivariate group lasso with covariance estimation

Wilms, I., & Croux, C. (2017) · Journal of Applied Statistics · doi:10.1080/02664763.2017.1289503 · Cited by 16

Brain Tissue Segmentation Based on Diffusion MRI Using ℓ0 Sparse-Group Representation Classification

Yap, P., Zhang, Y., & Shen, D. G. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_16 · Cited by 16

Learning Robust Auto-Encoders With Regularizer for Linearity and Sparsity

Shi, Y., Lei, M., Ma, R., & Niu, L. (2019) · IEEE Access · doi:10.1109/access.2019.2895884 · Cited by 16

Sample Complexity of Block-Sparse System Identification Problem

Fattahi, S., & Sojoudi, S. (2021) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2021.3089141 · Cited by 16

Machine Learning Panel Data Regressions with an Application to Nowcasting Price Earnings RatiosPreprint

Babii, A., Ball, R. T., Ghysels, É., & Striaukas, J. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3670847 · Cited by 16

Robust Temporal Smoothness in Multi-Task Learning

Zhou, M., Zhang, Y., Yang, Y., Liu, T., & Yang, P. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26351 · Cited by 15

Joint Network Topology Inference in the Presence of Hidden Nodes

Navarro, M., Rey, S., Buciulea, A., Marques, A. G., & Segarra, S. (2024) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2024.3392348 · Cited by 15

Modeling the Cholesky factors of covariance matrices of multivariate longitudinal data

Kohli, P., Garcia, T. P., & Pourahmadi, M. (2015) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2015.11.014 · Cited by 15

Isotropic sparse regularization for spherical harmonic representations of random fields on the sphere

Gia, Q. T. L., Sloan, I. H., Womersley, R. S., & Wang, Y. G. (2019) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2019.01.005 · Cited by 15

Feature Selection by Canonical Correlation Search in High-Dimensional Multiresponse Models With Complex Group Structures

Luo, S., & Chen, Z. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1609972 · Cited by 15

PLS for Big Data: A unified parallel algorithm for regularised group PLS

de Micheaux, P. L., Liquet, B., & Sutton, M. A. (2019) · Statistics Surveys · doi:10.1214/19-ss125 · Cited by 15

Hypernetwork Construction and Feature Fusion Analysis Based on Sparse Group Lasso Method on fMRI Dataset

Li, Y., Sun, C., Li, P., Zhao, Y., Mensah, G. K., Xu, Y. J., Guo, H., & Chen, J. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00060 · Cited by 15

A General Framework for Estimation and Inference From Clusters of Features

Reid, S., Taylor, J., & Tibshirani, R. J. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2016.1246368 · Cited by 15

A coordinate descent algorithm for computing penalized smooth quantile regression

Mkhadri, A., Ouhourane, M., & Oualkacha, K. (2016) · Statistics and Computing · doi:10.1007/s11222-016-9659-9 · Cited by 15

Interpretable sparse SIR for functional data

Picheny, V., Servien, R., & Vialaneix, N. (2018) · Statistics and Computing · doi:10.1007/s11222-018-9806-6 · Cited by 15

Geometric ergodicity of Gibbs samplers in Bayesian penalized regression models

Vats, D. (2017) · Electronic Journal of Statistics · doi:10.1214/17-ejs1351 · Cited by 15

Group regularization for zero‐inflated negative binomial regression models with an application to health care demand in Germany

Chatterjee, S., Chowdhury, S., Mallick, H., Banerjee, P., & Garai, B. (2018) · Statistics in Medicine · doi:10.1002/sim.7804 · Cited by 15

Supervised dynamic mode decomposition via multitask learning

Fujii, K., & Kawahara, Y. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.02.010 · Cited by 15

Group-sparse SVD Models via $L_1$- and $L_0$-norm Penalties and Their Applications in Biological Data

Min, W., Liu, J., & Zhang, S. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2932063 · Cited by 15

Hierarchical Group Sparse Regularization for Deep Convolutional Neural Networks

Mitsuno, K., Miyao, J., & Kurita, T. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207531 · Cited by 15

Functional sparsity: global versus local

Wang, H., & Kai, B. (2014) · Statistica Sinica · doi:10.5705/ss.2014.035 · Cited by 15

Principal component‐guided sparse regression

Tay, J. K., Friedman, J. H., & Tibshirani, R. J. (2021) · Canadian Journal of Statistics · doi:10.1002/cjs.11617 · Cited by 15

Learning Combination of Graph Filters for Graph Signal Modeling

Hua, F., Richard, C., Chen, J., Wang, H., Borgnat, P., & Gonçalvès, P. (2019) · IEEE Signal Processing Letters · doi:10.1109/lsp.2019.2954981 · Cited by 15

Distributed Tikhonov regularization for ill-posed inverse problems from a Bayesian perspective

Daniela Calvetti, A., & Somersalo, E. (2025) · Computational Optimization and Applications · doi:10.1007/s10589-025-00675-y · Cited by 14

New Theory of Discriminant Analysis

Shinmura, S. (2016) · Springer eBooks · doi:10.1007/978-981-10-2164-0_1 · Cited by 14

Machine learning methods for the detection of explosives, drugs and precursor chemicals gathered using a colorimetric sniffer sensor

Francis, D. P., Laustsen, M., Dossi, E., Treiberg, T., Hardy, I., Shiv, S. H., Hansen, B. S., Mogensen, J., Jakobsen, M. H., & Alstrøm, T. S. (2023) · Analytical Methods · doi:10.1039/d3ay00247k · Cited by 14

Cost-sensitive sparse group online learning for imbalanced data streams

Chen, Z., Sheng, V. S., Edwards, A., & Zhang, K. (2023) · Machine Learning · doi:10.1007/s10994-023-06403-z · Cited by 14

Laplace Group Sensing for Acoustic Models

Chien, J. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2412466 · Cited by 14

A Bayesian hierarchical model for identifying significant polygenic effects while controlling for confounding and repeated measures

McMahan, C. S., Baurley, J. W., Bridges, W. C., Joyner, C. N., Kacamarga, M. F., Lund, R., Pardamean, C. I., & Pardamean, B. (2017) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2017-0044 · Cited by 14

A Message Passing Approach to Acquire mm-Wave Channel State Information Based on Out-of-Band Data

Xiu, Y., Wang, W., & Zhang, Z. (2018) · IEEE Access · doi:10.1109/access.2018.2855688 · Cited by 14

MicroRNA dysregulational synergistic network: discovering microRNA dysregulatory modules across subtypes in non-small cell lung cancers

Tran, N., Abhyankar, V. V., Nguyen, K. T., Weidanz, J. A., & Gao, J. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2536-0 · Cited by 14

Group sparse recovery in impulsive noise via alternating direction method of multipliers

Wang, J., Huang, J., Zhang, F., & Wang, W. (2019) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2019.04.002 · Cited by 14

Emotion Recognition and EEG Analysis Using ADMM-Based Sparse Group Lasso

Puk, K. M., Wang, S., Rosenberger, J. M., Gandy, K. C., Harris, H. N., Peng, Y. B., Nordberg, A., Lehmann, P., Tommerdahl, J. M., & Chiao, J. ‐. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2943551 · Cited by 14

Granger Causality Detection in High-Dimensional Systems Using Feedforward Neural Networks

Calvo-Pardo, H., Mancini, T., & Olmo, J. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.10.004 · Cited by 15

Compressing Deep Neural Networks for Efficient Speech Enhancement

Tan, K., & Wang, D. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413536 · Cited by 14

Linear Regression

Lederer, J. (2021) · Springer texts in statistics · doi:10.1007/978-3-030-73792-4_2 · Cited by 16

A sparse fused group lasso regression model for fourier-transform infrared spectroscopic data with application to purity prediction in olive oil blends

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