{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T05:21:40Z","timestamp":1773724900087,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12371281"],"award-info":[{"award-number":["12371281"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic Science Center of National Science Foundation of China","award":["71988101"],"award-info":[{"award-number":["71988101"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s00521-026-11868-9","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T01:56:03Z","timestamp":1771898163000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sparse optimization for transfer learning: a $$L_0$$-regularized framework for multi-source domain adaptation"],"prefix":"10.1007","volume":"38","author":[{"given":"Chenqi","family":"Gong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"key":"11868_CR1","doi-asserted-by":"crossref","unstructured":"Torrey L, Shavlik J (2010) Transfer learning. In: Handbook of Research on Machine Learning Applications. pp 242-264","DOI":"10.4018\/978-1-60566-766-9.ch011"},{"key":"11868_CR2","unstructured":"Devlin J, Chang M, Lee K et al (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp 4171-4186"},{"key":"11868_CR3","doi-asserted-by":"crossref","unstructured":"Dettmers T, Pagnoni A, Holtzman A et al (2023) QLORA: efficient finetuning of quantized LLMs. In: Proceedings of the 37th International Conference on Neural Information Processing Systems, pp. 10088-10115","DOI":"10.52202\/075280-0441"},{"issue":"10","key":"11868_CR4","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359. https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11868_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105390","volume":"193","author":"Z Liu","year":"2020","unstructured":"Liu Z, Li X, Qiao L et al (2020) A cross-region transfer learning method for classification of community service cases with small datasets. Knowl-Based Syst 193:105390. https:\/\/doi.org\/10.1016\/j.knosys.2019.105390","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"11868_CR6","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1214\/20-AOS1949","volume":"49","author":"TT Cai","year":"2021","unstructured":"Cai TT, Wei H (2021) Transfer learning for nonparametric classification: minimax rate and adaptive classifier. Ann Stat 49(1):100\u2013128. https:\/\/doi.org\/10.1214\/20-AOS1949","journal-title":"Ann Stat"},{"key":"11868_CR7","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.artint.2013.01.003","volume":"197","author":"W Pan","year":"2013","unstructured":"Pan W, Yang Q (2013) Transfer learning in heterogeneous collaborative filtering domains. Artif Intell 197:39\u201355. https:\/\/doi.org\/10.1016\/j.artint.2013.01.003","journal-title":"Artif Intell"},{"key":"11868_CR8","unstructured":"Hajiramezanali E, Dadaneh S Z, Karbalayghareh A et al (2018) Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. pp 9133-9142"},{"key":"11868_CR9","doi-asserted-by":"crossref","unstructured":"Oquab M, Bottou L, Laptev I et al (2014) Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. pp 1717-1724","DOI":"10.1109\/CVPR.2014.222"},{"issue":"5","key":"11868_CR10","doi-asserted-by":"publisher","first-page":"3246","DOI":"10.1109\/TGRS.2019.2951445","volume":"58","author":"X He","year":"2020","unstructured":"He X, Chen Y, Ghamisi P (2020) Heterogeneous transfer learning for hyperspectral image classification based on convolutional neural network. IEEE Trans Geosci Remote Sens 58(5):3246\u20133263. https:\/\/doi.org\/10.1109\/TGRS.2019.2951445","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"11868_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106258","volume":"204","author":"S Wang","year":"2020","unstructured":"Wang S, Zhang L, Fu J (2020) Adversarial transfer learning for cross-domain visual recognition. Knowl-Based Syst 204:106258. https:\/\/doi.org\/10.1016\/j.knosys.2020.106258","journal-title":"Knowl-Based Syst"},{"issue":"6","key":"11868_CR12","doi-asserted-by":"publisher","first-page":"7201","DOI":"10.1007\/s10489-022-03893-7","volume":"53","author":"T Choudhary","year":"2023","unstructured":"Choudhary T, Gujar S, Goswami A et al (2023) Deep learning-based important weights-only transfer learning approach for COVID-19 CT-scan classification. Appl Intell 53(6):7201\u20137215. https:\/\/doi.org\/10.1007\/s10489-022-03893-7","journal-title":"Appl Intell"},{"key":"11868_CR13","doi-asserted-by":"publisher","first-page":"2314","DOI":"10.1007\/s10489-023-05249-1","volume":"54","author":"A Gyasi-Agyei","year":"2024","unstructured":"Gyasi-Agyei A (2024) Detection of explosives in dustbins using deep transfer learning based multiclass classifiers. Appl Intell 54:2314\u20132347. https:\/\/doi.org\/10.1007\/s10489-023-05249-1","journal-title":"Appl Intell"},{"key":"11868_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2023.113924","volume":"301","author":"Y Ma","year":"2024","unstructured":"Ma Y, Chen S, Ermon S et al (2024) Transfer learning in environmental remote sensing. Remote Sens Environ 301:113924. https:\/\/doi.org\/10.1016\/j.rse.2023.113924","journal-title":"Remote Sens Environ"},{"key":"11868_CR15","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/s10109-024-00455-y","volume":"27","author":"D Murakami","year":"2025","unstructured":"Murakami D, Kajita M, Kajita S (2025) Spatial process-based transfer learning for prediction problems. J Geogr Syst 27:147\u2013166. https:\/\/doi.org\/10.1007\/s10109-024-00455-y","journal-title":"J Geogr Syst"},{"issue":"6","key":"11868_CR16","doi-asserted-by":"publisher","first-page":"3618","DOI":"10.1214\/21-AOS2102","volume":"49","author":"HWJ Reeve","year":"2021","unstructured":"Reeve HWJ, Cannings TI, Samworth RJ (2021) Adaptive transfer learning. Ann Stat 49(6):3618\u20133649. https:\/\/doi.org\/10.1214\/21-AOS2102","journal-title":"Ann Stat"},{"issue":"5","key":"11868_CR17","doi-asserted-by":"publisher","first-page":"2657","DOI":"10.1287\/mnsc.2020.3729","volume":"67","author":"H Bastani","year":"2020","unstructured":"Bastani H (2020) Predicting with proxies: transfer learning in high dimension. Manage Sci 67(5):2657\u20133320. https:\/\/doi.org\/10.1287\/mnsc.2020.3729","journal-title":"Manage Sci"},{"issue":"1","key":"11868_CR18","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1111\/rssb.12479","volume":"84","author":"S Li","year":"2021","unstructured":"Li S, Cai TT, Li H (2021) Transfer learning for high-dimensional linear regression: Prediction, estimation and minimax optimality. J R Stat Soc Ser B Stat Methodol 84(1):149\u2013173. https:\/\/doi.org\/10.1111\/rssb.12479","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"issue":"544","key":"11868_CR19","doi-asserted-by":"publisher","first-page":"2684","DOI":"10.1080\/01621459.2022.2071278","volume":"118","author":"Y Tian","year":"2022","unstructured":"Tian Y, Feng Y (2022) Transfer learning under high-dimensional generalized linear models. J Am Stat Assoc 118(544):2684\u20132697. https:\/\/doi.org\/10.1080\/01621459.2022.2071278","journal-title":"J Am Stat Assoc"},{"issue":"543","key":"11868_CR20","doi-asserted-by":"publisher","first-page":"2171","DOI":"10.1080\/01621459.2022.2044333","volume":"118","author":"S Li","year":"2023","unstructured":"Li S, Cai TT, Li H (2023) Transfer Learning in Large-Scale Gaussian Graphical Models with False Discovery Rate Control. J Am Stat Assoc 118(543):2171\u20132183. https:\/\/doi.org\/10.1080\/01621459.2022.2044333","journal-title":"J Am Stat Assoc"},{"key":"11868_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109535","volume":"140","author":"Y Gao","year":"2023","unstructured":"Gao Y, Yang Y (2023) Transfer learning on stratified data: Joint estimation transferred from strata. Pattern Recogn 140:109535. https:\/\/doi.org\/10.1016\/j.patcog.2023.109535","journal-title":"Pattern Recogn"},{"issue":"10","key":"11868_CR22","first-page":"1","volume":"19","author":"J Huang","year":"2018","unstructured":"Huang J, Jiao Y, Liu Y et al (2018) A constructive approach to $$L_0$$ penalized regression. J Mach Learn Res 19(10):1\u201337","journal-title":"J Mach Learn Res"},{"issue":"1","key":"11868_CR23","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.ejor.2022.05.041","volume":"305","author":"S Dai","year":"2023","unstructured":"Dai S (2023) Variable selection in convex quantile regression: $$L_1$$-norm or $$L_0$$-norm regularization? Eur J Oper Res 305(1):338\u2013355. https:\/\/doi.org\/10.1016\/j.ejor.2022.05.041","journal-title":"Eur J Oper Res"},{"key":"11868_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110024","volume":"146","author":"H Ming","year":"2024","unstructured":"Ming H, Yang H (2024) $$L_0$$ regularized logistic regression for large-scale data. Pattern Recogn 146:110024. https:\/\/doi.org\/10.1016\/j.patcog.2023.110024","journal-title":"Pattern Recogn"},{"issue":"2","key":"11868_CR25","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1214\/aos\/1176344136","volume":"6","author":"G Schwarz","year":"1978","unstructured":"Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461\u2013464. https:\/\/doi.org\/10.1214\/aos\/1176344136","journal-title":"Ann Stat"},{"issue":"3","key":"11868_CR26","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1093\/biomet\/asn034","volume":"95","author":"J Chen","year":"2008","unstructured":"Chen J, Chen Z (2008) Extended Bayesian information criteria for model selection with large model spaces. Biometrika 95(3):759\u2013771. https:\/\/doi.org\/10.1093\/biomet\/asn034","journal-title":"Biometrika"},{"issue":"5","key":"11868_CR27","doi-asserted-by":"publisher","first-page":"2505","DOI":"10.1214\/13-AOS1159","volume":"41","author":"L Wang","year":"2013","unstructured":"Wang L, Kim Y, Li R (2013) Calibrating non-convex penalized regression in ultra-high dimension. Ann Stat 41(5):2505\u20132536. https:\/\/doi.org\/10.1214\/13-AOS1159","journal-title":"Ann Stat"},{"issue":"202","key":"11868_CR28","first-page":"1","volume":"23","author":"J Zhu","year":"2022","unstructured":"Zhu J, Wang X, Hu L et al (2022) Abess: a fast best-subset selection library in Python and R. J Mach Learn Res 23(202):1\u20137","journal-title":"J Mach Learn Res"},{"key":"11868_CR29","doi-asserted-by":"crossref","unstructured":"Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33(1), 1\u201322.https:\/\/doi.org\/10.18637\/jss.v033.i01","DOI":"10.18637\/jss.v033.i01"},{"issue":"3","key":"11868_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2024.104056","volume":"62","author":"MI Hossain","year":"2025","unstructured":"Hossain MI, Akhter S, Mahbub NI et al (2025) Why logit distillation works: a novel knowledge distillation technique by deriving target augmentation and logits distortion. Inf Process Manag 62(3):104056. https:\/\/doi.org\/10.1016\/j.ipm.2024.104056","journal-title":"Inf Process Manag"},{"key":"11868_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111579","volume":"159","author":"MI Hossain","year":"2025","unstructured":"Hossain MI, Akhter S, Hong CS et al (2025) PURF: improving teacher representations by imposing smoothness constraints for knowledge distillation. Appl Soft Comput 159:111579. https:\/\/doi.org\/10.1016\/j.asoc.2024.111579","journal-title":"Appl Soft Comput"},{"key":"11868_CR32","unstructured":"Hossain M I, Akhter S, Hong C S et al (2025) Single Teacher, Multiple Perspectives: Teacher Knowledge Augmentation for Enhanced Knowledge Distillation. In: International Conference on Representation Learning. pp 91583-91594"},{"key":"11868_CR33","doi-asserted-by":"publisher","unstructured":"Hossain M I, Akhter S, Hong C S et al (2025) Complexity-Aware Dynamic Gradient Shifting: A Novel Soft Supervision Training Strategy for 3D Pose Estimation and Regression Learning. In: IEEE Transactions on Circuits and Systems for Video Technology. https:\/\/doi.org\/10.1109\/TCSVT.2025.3563932","DOI":"10.1109\/TCSVT.2025.3563932"},{"key":"11868_CR34","doi-asserted-by":"crossref","unstructured":"Akhter S, Hossain M I, Hossain M D et al (2023) Enhancing Cloud Service Failure Prediction Via Temporal Relationship Distillation. In: 2023 International Conference on Computational Science and Computational Intelligence. pp 1061-1067","DOI":"10.1109\/CSCI62032.2023.00175"},{"key":"11868_CR35","doi-asserted-by":"publisher","first-page":"17285","DOI":"10.1007\/s00521-024-09933-2","volume":"36","author":"S Akhter","year":"2024","unstructured":"Akhter S, Hossain MI, Hossain MD (2024) EnParaNet: a novel deep learning architecture for faster prediction using low-computational resource devices. Neural Comput Appl 36:17285\u201317309. https:\/\/doi.org\/10.1007\/s00521-024-09933-2","journal-title":"Neural Comput Appl"},{"key":"11868_CR36","doi-asserted-by":"publisher","unstructured":"Redmond M (2002) Communities and Crime. UCI Machine Learning Repository. Available at: https:\/\/doi.org\/10.24432\/C53W3X","DOI":"10.24432\/C53W3X"},{"key":"11868_CR37","doi-asserted-by":"publisher","unstructured":"Chen S (2017) Beijing Multi-Site Air Quality. UCI Machine Learning Repository. Available at: https:\/\/doi.org\/10.24432\/C5RK5G","DOI":"10.24432\/C5RK5G"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11868-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-026-11868-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11868-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T03:40:48Z","timestamp":1773718848000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-026-11868-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":37,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["11868"],"URL":"https:\/\/doi.org\/10.1007\/s00521-026-11868-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"10 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"74"}}