{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T08:46:35Z","timestamp":1791449195263,"version":"4.3.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030326913","type":"print"},{"value":"9783030326920","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-32692-0_30","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T12:04:21Z","timestamp":1570622661000},"page":"256-264","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI"],"prefix":"10.1007","author":[{"given":"Wenwen","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Lou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiping","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"1","key":"30_CR1","first-page":"1","volume":"3","author":"S Boyd","year":"2011","unstructured":"Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends\u00ae Mach. Learn. 3(1), 1\u2013122 (2011)","journal-title":"Found. Trends\u00ae Mach. Learn."},{"issue":"3","key":"30_CR2","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1145\/1970392.1970395","volume":"58","author":"EJ Cand\u00e8s","year":"2011","unstructured":"Cand\u00e8s, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM (JACM) 58(3), 11 (2011)","journal-title":"J. ACM (JACM)"},{"key":"30_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/978-3-319-24553-9_84","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"M Eickenberg","year":"2015","unstructured":"Eickenberg, M., Dohmatob, E., Thirion, B., Varoquaux, G.: Grouping total variation and sparsity: statistical learning with segmenting penalties. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 685\u2013693. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24553-9_84"},{"key":"30_CR4","doi-asserted-by":"publisher","DOI":"10.1201\/b18401","volume-title":"Statistical Learning with Sparsity: The Lasso and Generalizations","author":"T Hastie","year":"2015","unstructured":"Hastie, T., Tibshirani, R., Wainwright, M.: Statistical Learning with Sparsity: The Lasso and Generalizations. CRC Press, Boca Raton (2015)"},{"key":"30_CR5","volume-title":"Functional Magnetic Resonance Imaging","author":"SA Huettel","year":"2004","unstructured":"Huettel, S.A., Song, A.W., McCarthy, G.: Functional Magnetic Resonance Imaging, vol. 1. Sinauer Associates, Sunderland (2004)"},{"issue":"1","key":"30_CR6","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1137\/110837711","volume":"34","author":"ME Kilmer","year":"2013","unstructured":"Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: a theoretical and computational framework with applications in imaging. SIAM J. Matrix Anal. Appl. 34(1), 148\u2013172 (2013)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Lu, C., Feng, J., Liu, W., Lin, Z., Yan, S.: Tensor robust principal component analysis with a new tensor nuclear norm. IEEE Trans. PAMI (2019)","DOI":"10.1109\/TPAMI.2019.2891760"},{"key":"30_CR8","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3389\/fninf.2013.00012","volume":"7","author":"RA Poldrack","year":"2013","unstructured":"Poldrack, R.A., Barch, D.M., Mitchell, J., et al.: Toward open sharing of task-based fMRI data: the OpenfMRI project. Front. Neuroinf. 7, 12 (2013)","journal-title":"Front. Neuroinf."},{"key":"30_CR9","unstructured":"Rao, N., Cox, C., Nowak, R., et al.: Sparse overlapping sets lasso for multitask learning and its application to fMRI analysis. In: NeurIPS, pp. 2202\u20132210 (2013)"},{"key":"30_CR10","unstructured":"Romera-Paredes, B., Pontil, M.: A new convex relaxation for tensor completion. In: NeurIPS, pp. 2967\u20132975 (2013)"},{"issue":"2","key":"30_CR11","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1016\/j.neuroimage.2010.02.040","volume":"51","author":"S Ryali","year":"2010","unstructured":"Ryali, S., Supekar, K., Abrams, D.A., Menon, V.: Sparse logistic regression for whole-brain classification of fmri data. NeuroImage 51(2), 752\u2013764 (2010)","journal-title":"NeuroImage"},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Song, X., Lu, H.: Multilinear regression for embedded feature selection with application to fMRI analysis. In: AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.10871"},{"issue":"3","key":"30_CR13","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/BF02289464","volume":"31","author":"LR Tucker","year":"1966","unstructured":"Tucker, L.R.: Some mathematical notes on three-mode factor analysis. Psychometrika 31(3), 279\u2013311 (1966)","journal-title":"Psychometrika"},{"issue":"6","key":"30_CR14","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1109\/TSP.2016.2639466","volume":"65","author":"Z Zhang","year":"2017","unstructured":"Zhang, Z., Aeron, S.: Exact tensor completion using t-SVD. IEEE Trans. Sig. Process. 65(6), 1511\u20131526 (2017)","journal-title":"IEEE Trans. Sig. Process."},{"issue":"2","key":"30_CR15","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.) 67(2), 301\u2013320 (2005)","journal-title":"J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32692-0_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:03:29Z","timestamp":1728432209000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32692-0_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030326913","9783030326920"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32692-0_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/mlmi2019.web.unc.edu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"158","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"78","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"49% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}