{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:22:41Z","timestamp":1743063761350,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030609351"},{"type":"electronic","value":"9783030609368"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-60936-8_15","type":"book-chapter","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T10:05:52Z","timestamp":1602669952000},"page":"187-202","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["GTT: Guiding the Tensor Train Decomposition"],"prefix":"10.1007","author":[{"given":"Mao-Lin","family":"Li","sequence":"first","affiliation":[]},{"given":"K. Sel\u00e7uk","family":"Candan","sequence":"additional","affiliation":[]},{"given":"Maria Luisa","family":"Sapino","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,14]]},"reference":[{"key":"15_CR1","unstructured":"Batselier, K.: The trouble with tensor ring decompositions. CoRR abs\/1811.03813 (2018)"},{"issue":"3","key":"15_CR2","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1137\/17M1140480","volume":"39","author":"K Batselier","year":"2018","unstructured":"Batselier, K., Yu, W., Daniel, L., Wong, N.: Computing low-rank approximations of large-scale matrices with the tensor network randomized SVD. SIAM J. Matrix Anal. Appl. 39(3), 1221\u20131244 (2018)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Bedo, M.V.N., Ciaccia, P., Martinenghi, D., de Oliveira, D.: A k-skyband approach for feature selection. In: SISAP 2019, Newark, NJ, USA, Proceedings (2019)","DOI":"10.1007\/978-3-030-32047-8_15"},{"key":"15_CR4","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511781636","volume-title":"Data Management for Multimedia Retrieval","author":"KS Candan","year":"2010","unstructured":"Candan, K.S., Sapino, M.L.: Data Management for Multimedia Retrieval. Cambridge University Press, USA (2010)"},{"issue":"3","key":"15_CR5","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/BF02310791","volume":"35","author":"JD Carroll","year":"1970","unstructured":"Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of \"eckart-young\" decomposition. Psychometrika 35(3), 283\u2013319 (1970). https:\/\/doi.org\/10.1007\/BF02310791","journal-title":"Psychometrika"},{"key":"15_CR6","unstructured":"Chen, Y., Jin, X., Kang, B., Feng, J., Yan, S.: Sharing residual units through collective tensor factorization to improve deep neural networks. In: IJCAI-18"},{"key":"15_CR7","unstructured":"Dash, M., Choi, K., Scheuermann, P., Liu, H.: Feature selection for clustering - A filter solution. In: 2002 IEEE ICDM, 2002, Proceedings, pp. 115\u2013122 (2002)"},{"key":"15_CR8","unstructured":"Dash, M., Liu, H., Yao, J.: Dimensionality reduction of unsupervised data. In: IEEE International Conference on Tools with Artificial Intelligence (Nov 1997)"},{"key":"15_CR9","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2017)"},{"key":"15_CR10","unstructured":"Harshman, R.: Foundations of the parafac procedure: Models and conditions for an \u201cexplanatory\u201d multi-modal factor analysis. UCLA Working Papers in Phonetics, vol. 16 (1970)"},{"key":"15_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1007\/978-3-642-32153-5_16","volume-title":"Similarity Search and Applications","author":"ME Houle","year":"2012","unstructured":"Houle, M.E., Kashima, H., Nett, M.: Fast similarity computation in factorized tensors. In: Navarro, G., Pestov, V. (eds.) SISAP 2012. LNCS, vol. 7404, pp. 226\u2013239. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-32153-5_16"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Huang, S., Candan, K.S., Sapino, M.L.: Bicp: block-incremental CP decomposition with update sensitive refinement. In: CIKM 2016. ACM, New York (2016)","DOI":"10.1145\/2983323.2983717"},{"key":"15_CR13","unstructured":"Imaizumi, M., Maehara, T., Hayashi, K.: On tensor train rank minimization: Statistical efficiency and scalable algorithm. In: NIPS, pp. 3930\u20133939 (2017)"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Jeon, I., Papalexakis, E.E., Kang, U., Faloutsos, C.: Haten2: Billion-scale tensor decompositions. In: 2015 IEEE 31st ICDE, pp. 1047\u20131058 (2015)","DOI":"10.1109\/ICDE.2015.7113355"},{"issue":"1","key":"15_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10618-015-0401-6","volume":"30","author":"M Kim","year":"2016","unstructured":"Kim, M., Candan, K.S.: Decomposition-by-normalization (DBN): leveraging approximate functional dependencies for efficient CP and tucker decompositions. Data Min. Knowl. Disc. 30(1), 1\u201346 (2016)","journal-title":"Data Min. Knowl. Disc."},{"key":"15_CR16","doi-asserted-by":"publisher","unstructured":"Ko, C.Y., Lin, R., Li, S., Wong, N.: Misc: Mixed strategies crowdsourcing. In: IJCAI-19, pp. 1394\u20131400 (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/193","DOI":"10.24963\/ijcai.2019\/193"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1), 273\u2013324 (1997). Relevance","DOI":"10.1016\/S0004-3702(97)00043-X"},{"issue":"3","key":"15_CR18","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1137\/07070111X","volume":"51","author":"TG Kolda","year":"2009","unstructured":"Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455\u2013500 (2009). https:\/\/doi.org\/10.1137\/07070111X","journal-title":"SIAM Rev."},{"key":"15_CR19","unstructured":"Li, L., Yu, W., Batselier, K.: Faster tensor train decomposition for sparse data. ArXiv (2019)"},{"key":"15_CR20","unstructured":"Mickelin, O., Karaman, S.: Tensor ring decomposition. CoRR abs\/1807.02513 (2018)"},{"key":"15_CR21","unstructured":"Novikov, A., Podoprikhin, D., Osokin, A., Vetrov, D.: Tensorizing neural networks. In: NIPS 2015, pp. 442\u2013450. MIT Press, Cambridge (2015)"},{"key":"15_CR22","unstructured":"Novikov, A., Trofimov, M., Oseledets, I.: Exponential Machines (2016). arXiv e-prints arXiv:1605.03795"},{"issue":"5","key":"15_CR23","doi-asserted-by":"publisher","first-page":"2295","DOI":"10.1137\/090752286","volume":"33","author":"I Oseledets","year":"2011","unstructured":"Oseledets, I.: Tensor-train decomposition. SIAM J. Sci. Comput. 33(5), 2295\u20132317 (2011). https:\/\/doi.org\/10.1137\/090752286","journal-title":"SIAM J. Sci. Comput."},{"issue":"3","key":"15_CR24","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/BF02289464","volume":"31","author":"L Tucker","year":"1966","unstructured":"Tucker, L.: Some mathematical notes on three-mode factor analysis. Psychometrika 31(3), 279\u2013311 (1966)","journal-title":"Psychometrika"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Yamaguchi, Y., Hayashi, K.: Tensor decomposition with missing indices. In: IJCAI 2017, pp. 3217\u20133223. AAAI Press (2017)","DOI":"10.24963\/ijcai.2017\/449"},{"key":"15_CR26","unstructured":"Yu, L., Liu, H.: Feature selection for high-dimensional data: A fast correlation-based filter solution. In: ICML 2003, vol. 2, pp. 856\u2013863 (2003)"},{"key":"15_CR27","unstructured":"Zhao, Q., Zhou, G., Xie, S., Zhang, L., Cichocki, A.: Tensor ring decomposition. CoRR abs\/1606.05535 (2016)"}],"container-title":["Lecture Notes in Computer Science","Similarity Search and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60936-8_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T13:43:08Z","timestamp":1710250988000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60936-8_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030609351","9783030609368"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60936-8_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SISAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Similarity Search and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Copenhagen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Denmark","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sisap2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.sisap.org\/2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50","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":"19","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":"12","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":"38% - 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":"3.9","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":"3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2 short papers accepted for the SISAP 2020 Doctoral Symposium are also included. The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}