The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Regularization and Variable Selection Via the Elastic Net
Open accessAbstract
Summary: We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together. The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the p≫n case. An algorithm called LARS-EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lasso.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For example, if there are disproportionately few samples relative to the number of features in the dataset or if some of the input features are sparsely represented, early or late fusion is preferred because they can be implemented with traditional machine learning algorithms (e.g., Lasso and ElasticNet47) that are better suited for this type of data48.”
From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [47]
In the citing paper
“We used the elastic net algorithm, given substantial evidence that penalization improves prediction accuracy,25–27 alongside its well-established accuracy and robustness28,29 and ability to maintain clinical interpretability compared with less transparent machine learning algorithms (eg, random forests, neural networks).”
From A pilot study using frequent inpatient assessments of suicidal thinking to predict short-term postdischarge suicidal behavior (2021) · cites this paper as [29]
Citations
Cites
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
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Self-citationGene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Sparse Principal Component Analysis
Zou, H., Hastie, T., & Tibshirani, R. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x113430 · Cited by 3,152
Self-citationVariable Selection via Gibbs Sampling
George, E. I., & McCulloch, R. E. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476353 · Cited by 2,828
'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns
Hastie, T., Tibshirani, R., Eisen, M. B., Alizadeh, A., Levy, R., Staudt, L., Chan, W. C., Botstein, D., & Brown, P. (2000) · Genome Biology · doi:10.1186/gb-2000-1-2-research0003 · Cited by 547
Self-citation
Research works printed but not linked: 21
- Breiman, 1996, Heuristics of instability and stabilization in model selection, Ann. Statist., 24, 2350, 10.1214/aos/1032181158 · DOI
- Dettling, 2004, Finding predictive gene groups from microarray data, J. Multiv. Anal., 90, 106, 10.1016/j.jmva.2004.02.012 · DOI
- Díaz-Uriarte, 2003, Tech-nical Report
- Donoho, 1995, Wavelet shrinkage: asymptopia (with discussion)?, J. R. Statist. Soc., 57, 301, 10.1111/j.2517-6161.1995.tb02032.x · DOI
- Efron, 2004, Least angle regression, Ann. Statist., 32, 407, 10.1214/009053604000000067 · DOI · Paper page
- Fan, 2001, Variable selection via nonconcave penalized likelihood and its oracle properties, J. Am. Statist. Ass., 96, 1348, 10.1198/016214501753382273 · DOI
- Frank, 1993, A statistical view of some chemometrics regression tools, Technometrics, 35, 109, 10.1080/00401706.1993.10485033 · DOI
- Friedman, 1989, Regularized discriminant analysis, J. Am. Statist. Ass., 84, 249, 10.1080/01621459.1989.10478752 · DOI
- Friedman, 2004, Discussion of boosting papers, Ann. Statist., 32, 102
- Fu, 1998, Penalized regression: the bridge versus the lasso, J. Computnl Graph. Statist., 7, 397, 10.1080/10618600.1998.10474784 · DOI
- Golub, 1999, Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science, 286, 513, 10.1126/science.286.5439.531 · DOI · Paper page
- Hastie, 2003, Supervised harvesting of expression trees, Genome Biol., 2, 0003.1
- Hoerl, 1988, Encyclopedia of Statistical Sciences, 129
- Rosset, 2004, Boosting as a regularized path to a maximum margin classifier, J. Mach. Learn. Res., 5, 941
- Segal, 2003, Regression approach for microarray data analysis, J. Computnl Biol., 10, 961, 10.1089/106652703322756177 · DOI
- Stamey, 1989, Prostate specific antigen in the diagnosis and treatment of adenocarcinoma of the prostate ii: radical prostatectomy treated patients, J. Urol., 16, 1076, 10.1016/S0022-5347(17)41175-X · DOI
- Tibshirani, 2002, Diagnosis of multiple cancer types by shrunken centroids of gene expression, Proc. Natn. Acad. Sci. USA, 99, 6567, 10.1073/pnas.082099299 · DOI
- Tusher, 2001, Significance analysis of microarrays applied to transcriptional responses to ionizing radiation, Proc. Natn. Acad. Sci. USA, 98, 5116, 10.1073/pnas.091062498 · DOI · Paper page
- West, 2001, Predicting the clinical status of human breast cancer using gene expression profiles, Proc. Natn. Acad. Sci. USA, 98, 11462, 10.1073/pnas.201162998 · DOI
- Zhang, 2004, Statistical behavior and consistency of classification methods based on convex risk minimization, Ann. Statist., 32, 469, 10.1214/aos/1079120130 · DOI
- Zhu, 2004, Classification of gene microarrays by penalized logistic regression, Biostatistics, 5, 427, 10.1093/biostatistics/kxg046 · DOI
Cited by
Sorted by citations
Items 201–263 of 263
Imaging-wide association study: integrating imaging endophenotypes in GWAS
Xu, Z., Wu, C., & Pan, W. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.036 · Cited by 56
Barbeira, A. N., Dickinson, S. P., Bonazzola, R., Zheng, J., Wheeler, H. E., Torres, J. M., Torstenson, E. S., Shah, K. P., Garcia, T., Edwards, T. L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03621-1 · Cited by 69
Empirical Asset Pricing via Machine Learning
Gu, S., Kelly, B. T., & Xiu, D. (2018) · The Review of Financial Studies · doi:10.2139/ssrn.3159577 · Cited by 52
The DELCODE Study Group, Brueggen, K., Dyrba, M., Cardenas-Blanco, A., Schneider, A., Fliessbach, K., Buerger, K., Janowitz, D., Peters, O., Menne, F., et al. (2019) · Journal of Neurology · doi:10.1007/s00415-019-09429-3 · Cited by 51
Feature Selection Boosted by Unselected Features
Zheng, W., Chen, S., Fu, Z., Zhu, F., Yan, H., & Yang, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3058172 · Cited by 51
Green, G. S., Yang, H., Fujita, M., Taga, M., Regev, A., Menon, V., Bennett, D. A. A., Habib, N., & De Jager, P. L. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.083212 · Cited by 51
Lessons and tips for designing a machine learning study using EHR data
Arbet, J., Brokamp, C., Meinzen-Derr, J., Trinkley, K. E., & Spratt, H. M. (2021) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.513 · Cited by 46
Energy-Efficient Multi-Mode Compressed Sensing System for Implantable Neural Recordings
Suo, Y., Zhang, J., Xiong, T., Chin, P. S., Etienne-Cummings, R., & Tran, T. D. (2014) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2014.2359180 · Cited by 47
Lee, A., Inceoglu, I., Hauser, O., & Greene, M. (2022) · The Leadership Quarterly · doi:10.1016/j.leaqua.2020.101426 · Cited by 48
Sex similarities and differences in risk factors for recurrence of major depression
van Loo, H. M., Aggen, S. H., Gardner, C. O., & Kendler, K. S. (2018) · Psychological Medicine · doi:10.1017/s0033291717003178 · Cited by 45
Multiple risk factors predict recurrence of major depressive disorder in women
van Loo, H. M., Aggen, S. H., Gardner, C. O., & Kendler, K. S. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.03.045 · Cited by 45
Williams, L. M., Pines, A., Goldstein-Piekarski, A. N., Rosas, L. G., Kullar, M., Sacchet, M. D., Gevaert, O., Bailenson, J., Lavori, P. W., Dagum, P., et al. (2018) · Behaviour Research and Therapy · doi:10.1016/j.brat.2017.09.012 · Cited by 40
Phylogenetic Analysis Supports a Link between DUF1220 Domain Number and Primate Brain Expansion
Zimmer, F., & Montgomery, S. H. (2015) · Genome Biology and Evolution · doi:10.1093/gbe/evv122 · Cited by 36
Kim, M., Yang, J., Ahn, W.-Y., & Choi, H. J. (2021) · Journal of Medical Internet Research · doi:10.2196/27218 · Cited by 41
ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder
Chang, B., Choi, Y., Jeon, M., Lee, J., Han, K.-M., Kim, A., Ham, B.-J., & Kang, J. (2019) · Genes · doi:10.3390/genes10110907 · Cited by 35
Prediction of 3-year clinical course in CADASIL
Jouvent, E., Duchesnay, E., Hadj-Selem, F., De Guio, F., Mangin, J.-F., Hervé, D., Duering, M., Ropele, S., Schmidt, R., Dichgans, M., et al. (2016) · Neurology · doi:10.1212/wnl.0000000000003252 · Cited by 30
Cognition Test Battery Performance Is Associated with Simulated 6df Spacecraft Docking Performance
Basner, M., Moore, T. M., Hermosillo, E., Nasrini, J., Dinges, D. F., Gur, R. C., & Johannes, B. (2020) · Aerospace Medicine and Human Performance · doi:10.3357/amhp.5602.2020 · Cited by 29
Sparse Reduced-Rank Regression for Exploratory Visualisation of Paired Multivariate Data
Kobak, D., Bernaerts, Y., Weis, M. A., Scala, F., Tolias, A. S., & Berens, P. (2021) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/rssc.12494 · Cited by 24
Supersparse Linear Integer Models for Interpretable Classification
Ustun, B., Tracà, S., & Rudin, C. (2013) · arXiv · doi:10.48550/arxiv.1306.6677 · Cited by 26
A Machine Learning Tutorial for Operational Meteorology. Part I: Traditional Machine Learning
Chase, R. J., Harrison, D. R., Burke, A., Lackmann, G. M., & McGovern, A. (2022) · Weather and Forecasting · doi:10.1175/waf-d-22-0070.1 · Cited by 25
Cohen, Z. D., DeRubeis, R. J., Hayes, R., Watkins, E. R., Lewis, G., Byng, R., Byford, S., Crane, C., Kuyken, W., Dalgleish, T., et al. (2023) · Clinical Psychological Science · doi:10.1177/21677026221076832 · Cited by 25
Using machine learning to model trace behavioral data from a game‐based assessment
Auer, E. M., Mersy, G., Marin, S., Blaik, J., & Landers, R. N. (2022) · International Journal of Selection and Assessment · doi:10.1111/ijsa.12363 · Cited by 24
Talk2Me: Automated linguistic data collection for personal assessment
Komeili, M., Pou-Prom, C., Liaqat, D., Fraser, K. C., Yancheva, M., & Rudzicz, F. (2019) · PLOS ONE · doi:10.1371/journal.pone.0212342 · Cited by 23
Motor Capacities in Boys with High Functioning Autism: Which Evaluations to Choose?
BRICOUT, V.-A., PACE, M., DUMORTIER, L., MIGANEH, S., MAHISTRE, Y., & GUINOT, M. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8101521 · Cited by 23
Predicting domestic abuse (fairly) and police risk assessment
Turner, E., Brown, G., & Medina-Ariza, J. (2022) · Psychosocial Intervention · doi:10.5093/pi2022a11 · Cited by 22
Using Aggregate Patient Data at the Bedside via an On-Demand Consultation Service
Callahan, A., Gombar, S., Cahan, E. M., Jung, K., Steinberg, E., Polony, V., Morse, K., Tibshirani, R., Hastie, T., Harrington, R., et al. (2021) · NEJM Catalyst · doi:10.1056/cat.21.0224 · Cited by 24
Self-citationMacDonald, M. E., Williams, R. J., Rajashekar, D., Stafford, R. B., Hanganu, A., Sun, H., Berman, A. J. L., McCreary, C. R., Frayne, R., Forkert, N. D., et al. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.06.019 · Cited by 21
Genomic Selection for Small Grain Improvement
Rutkoski, J. E., Crain, J., Poland, J., & Sorrells, M. E. (2017) · Genomic Selection for Crop Improvement · doi:10.1007/978-3-319-63170-7_5 · Cited by 24
Teipel, S. J., Dyrba, M., Ballarini, T., Brosseron, F., Bruno, D., Buerger, K., Cosma, N.-C., Dechent, P., Dobisch, L., Düzel, E., et al. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-215196 · Cited by 20
Leveraging calcium score CT radiomics for heart failure risk prediction
Singh, P., Hoori, A., Freeze, J., Hu, T., Tashtish, N., Gilkeson, R., Li, S., Rajagopalan, S., Wilson, D. L., & Al-Kindi, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-77269-x · Cited by 19
Stablecoins, money market funds, and monetary policy
Aldasoro, I., Cornelli, G., Ferrari Minesso, M., Gambacorta, L., & Habib, M. M. (2025) · Economics Letters · doi:10.1016/j.econlet.2025.112203 · Cited by 18
Kim, A. A., Rachid Zaim, S., & Subbian, V. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104148 · Cited by 18
Chromatin accessibility dynamics of myogenesis at single cell resolution
Pliner, H. A., Packer, J., McFaline-Figueroa, J. L., Cusanovich, D. A., Daza, R., Srivatsan, S., Qiu, X., Jackson, D., Minkina, A., Adey, A., et al. (2017) · doi:10.1101/155473 · Cited by 16
Self-Tuned Descriptive Document Clustering Using a Predictive Network
Brockmeier, A. J., Mu, T., Ananiadou, S., & Goulermas, J. Y. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2781721 · Cited by 15
Zhu, Y., Tan, T. L., & Cheang, W. K. (2017) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2017.09.019 · Cited by 13
A Fair Individualized Polysocial Risk Score for Identifying Increased Social Risk in Type 2 Diabetes
Huang, Y., Guo, J., Donahoo, W. T., Lee, Y. A., Fan, Z., Lu, Y., Chen, W.-H., Tang, H., Bilello, L., Saguil, A. A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52960-9 · Cited by 14
Ji, N., Eckel, S. P., Foley, H., Yang, T., Lurmann, F., Grubbs, B. H., Habre, R., Bastain, T. M., Farzan, S. F., & Breton, C. V. (2024) · Environmental Research · doi:10.1016/j.envres.2024.118797 · Cited by 14
Beyond Medical Imaging - A Review of Multimodal Deep Learning in Radiology
Heiliger, L., Sekuboyina, A., Menze, B., Egger, J., & Kleesiek, J. (2022) · doi:10.36227/techrxiv.19103432 · Cited by 13
Berwian, I. M., Wenzel, J. G., Kuehn, L., Schnuerer, I., Seifritz, E., Stephan, K. E., Walter, H., & Huys, Q. J. M. (2022) · Scientific Reports · doi:10.1038/s41598-022-13893-9 · Cited by 13
Chiesa, S., Marconi, E., Dinapoli, N., Sanfilippo, M. Z., Ruggiero, A., Mastronuzzi, A., Panza, G., Serra, A., Massaccesi, M., Cacchione, A., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.621690 · Cited by 13
Xue, Y., Zhang, J., Li, C., Liu, X., Kuang, W., Deng, J., Wang, J., Tan, X., Li, S., & Li, C. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2022.940802 · Cited by 13
SIS-seq, a molecular ‘time machine’, connects single cell fate with gene programs
Tian, L., Schreuder, J., Zalcenstein, D., Tran, J., Kocovski, N., Su, S., Diakumis, P., Bahlo, M., Sargeant, T., Hodgkin, P. D., et al. (2018) · doi:10.1101/403113 · Cited by 10
Vai, B., Mazza, M. G., Cazzetta, S., Calesella, F., Aggio, V., Lorenzi, C., Zanardi, R., Poletti, S., Colombo, C., & Benedetti, F. (2021) · Journal of Affective Disorders Reports · doi:10.1016/j.jadr.2021.100254 · Cited by 13
Pairing Machine Learning and Clinical Psychology: How You Evaluate Predictive Performance Matters
Jacobucci, R., Littlefield, A. K., Millner, A. J., Kleiman, E., & Steinley, D. (2020) · doi:10.31234/osf.io/2yber · Cited by 12
Replication-based regularization approaches to diagnose Reinke’s edema by using voice recordings
Naranjo, L., Pérez, C. J., Campos-Roca, Y., & Madruga, M. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102162 · Cited by 11
Quantum computing and supervised machine learning
Oneto, L., Ridella, S., & Anguita, D. (2017) · Quantum Inspired Computational Intelligence · doi:10.1016/b978-0-12-804409-4.00002-4 · Cited by 11
Perfalk, E., Damgaard, J. G., Bernstorff, M., Hansen, L., Danielsen, A. A., & Østergaard, S. D. (2024) · Psychological Medicine · doi:10.1017/s0033291724002642 · Cited by 12
Deep Genomic Signature for early metastasis prediction in prostate cancer
Sharifi-Noghabi, H., Liu, Y., Erho, N., Shrestha, R., Alshalalfa, M., Davicioni, E., Collins, C. C., & Ester, M. (2018) · doi:10.1101/276055 · Cited by 11
Chromatin structure and context-dependent sequence features control prime editing efficiency
Kim, S., Yuan, J. B., Woods, W. S., Newton, D. A., Perez-Pinera, P., & Song, J. S. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1222112 · Cited by 12
The Impact of Data Quality on Software Testing Effort Prediction
Radliński, Ł. (2023) · Electronics · doi:10.3390/electronics12071656 · Cited by 8
Sparse principal component analysis for high-dimensional stationary time series
Fujimori, K., Goto, Y., Liu, Y., & Taniguchi, M. (2023) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12664 · Cited by 7
Dementia is Associated With Inferior Outcomes Following Emergency General Surgery
Verma, A., Branche, C., Chervu, N. L., Sakowitz, S., Bakhtiyar, S. S., Hadaya, J., & Benharash, P. (2023) · The American Surgeon™ · doi:10.1177/00031348231175447 · Cited by 6
Jiwani, Z., Goldberg, S. B., Stroud, J., Young, J., Curtin, J., Dunne, J. D., Simonsson, O., Webb, C. A., Carhart-Harris, R., & Schlosser, M. (2025) · PLOS ONE · doi:10.1371/journal.pone.0310160 · Cited by 6
A new multiple kernel-based regularization method for identification of delay linear dynamic systems
Chen, X., Mao, Z., & Jia, R. (2020) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2020.103971 · Cited by 5
Spedicato, G., Dutang, C., & Petrini, L. (2018) · Variance · doi:10.66573/001c.116043 · Cited by 5
Lughofer, E., Pollak, R., Zavoianu, A.-C., Meyer-Heye, P., Zorrer, H., Eitzinger, C., Lehner, J., Radauer, T., & Pratama, M. (2018) · 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) · doi:10.1109/eais.2018.8397186 · Cited by 4
Peciulis, T., & Vasiliauskaite, A. (2024) · Economics and Culture · doi:10.2478/jec-2024-0006 · Cited by 3
Ferariu, A., Chang, H., Kumar, A., Sahl, A., Gorka, S., Wang, L., Thompson, W. K., & Zhang, F. (2025) · doi:10.1101/2025.03.12.25323356 · Cited by 3
The More, the Better? Predicting Stock Returns with Local and Global Data
Cakici, N., & Zaremba, A. (2025) · doi:10.2139/ssrn.5181449 · Cited by 2
Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer’s Disease
Xie, L., Raj, Y., Varathan, P., He, B., Yu, M., Nho, K., Salama, P., Saykin, A. J., & Yan, J. (2024) · Journal of Alzheimer's Disease · doi:10.3233/jad-240098 · Cited by 2
Olinger, B., Anerillas, C., Herman, A. B., Tsitsipatis, D., Banarjee, R., Tanaka, T., Candia, J., Maragkakis, M., Bandinelli, S., Walker, K. A., et al. (2026) · doi:10.64898/2026.02.06.26345739
Gene expression associated with PTSD in World Trade Center responders: An RNA sequencing study
Kuan, P.-F., Waszczuk, M. A., Kotov, R., Clouston, S., Yang, X., Singh, P. K., Glenn, S. T., Cortes Gomez, E., Wang, J., Bromet, E., et al. (2017) · Translational Psychiatry · doi:10.1038/s41398-017-0050-1 · Cited by 80
Metadata
- Authors
- Zou, H., & Hastie, T.
- Affiliations
- Stanford University (US)
- Corresponding author
- Hastie, Trevor
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-03-09
- Volume
- 67
- Issue
- 2
- Pages
- 301-320
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2122825543
- Cited by
- 20,957
- References
- 7
- Citations by year
- 2026: 1933 · 2025: 1906 · 2024: 1653 · 2023: 1540 · 2022: 1664 · 2021: 1924 · 2020: 1702 · 2019: 1536 · 2018: 1338 · 2017: 1092 · 2016: 949 · 2015: 884 · 2014: 727 · 2013: 585 · 2012: 468
- FWCI
- 79.40
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation (DMS-0204162)
- National Institutes of Health (RO1-EB0011988-08)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Signal Processing › Sparse and Compressive Sensing Techniques
- Keywords
- elastic net, regularization, feature selection, LASSO, high-dimensional data, correlated predictors, grouping effect, regularization path, sparse representation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
