{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T04:29:36Z","timestamp":1745468976007,"version":"3.40.4"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031758225"},{"type":"electronic","value":"9783031758232"}],"license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-75823-2_10","type":"book-chapter","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T20:33:24Z","timestamp":1729802004000},"page":"111-125","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bayesian Estimation Approaches for\u00a0Local Intrinsic Dimensionality"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8130-270X","authenticated-orcid":false,"given":"Zaher","family":"Joukhadar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2793-6680","authenticated-orcid":false,"given":"Hanxun","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0885-0643","authenticated-orcid":false,"given":"Sarah Monazam","family":"Erfani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0266-3492","authenticated-orcid":false,"given":"Ricardo J. G. B.","family":"Campello","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8486-8015","authenticated-orcid":false,"given":"Michael E.","family":"Houle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3769-3811","authenticated-orcid":false,"given":"James","family":"Bailey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Amsaleg, L., Chelly, O., Furon, T., Girard, S., Houle, M.E., Kawarabayashi, K., Nett, M.: Extreme-value-theoretic estimation of local intrinsic dimensionality. DMKD (2018)","DOI":"10.1007\/s10618-018-0578-6"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Anderberg, A., Bailey, J., Campello, R.J.G., Houle, M.E., Marques, H., Radovanovi\u0107, M., Zimek, A.: Dimensionality-aware outlier detection: theoretical and experimental analysis. In: (SDM24) (2024)","DOI":"10.1137\/1.9781611978032.75"},{"key":"10_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/e24091220","author":"J Bailey","year":"2022","unstructured":"Bailey, J., Houle, M., Ma, X.: Local intrinsic dimensionality, entropy and statistical divergences. Ent. (2022). https:\/\/doi.org\/10.3390\/e24091220","journal-title":"Ent."},{"issue":"1","key":"10_CR4","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1214\/aoms\/1177698503","volume":"39","author":"L Brown","year":"1968","unstructured":"Brown, L.: Inadmissibility of the usual estimators of scale parameters in problems with unknown location and scale parameters. Ann. Math. Stat. 39(1), 29\u201348 (1968)","journal-title":"Ann. Math. Stat."},{"key":"10_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2015\/759567","volume":"2015","author":"P Campadelli","year":"2015","unstructured":"Campadelli, P., Casiraghi, E., Ceruti, C., Rozza, A.: Intrinsic dimension estimation: relevant techniques and a benchmark framework. Math. Probl. Eng. 2015, 1\u201321 (2015)","journal-title":"Math. Probl. Eng."},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Denti, F., Doimo, D., Laio, A., Mira, A.: The generalized ratios intrinsic dimension estimator. Sci. Rep. 12(1) (2022)","DOI":"10.1038\/s41598-022-20991-1"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Facco, E., d\u2019Errico, M., Rodriguez, A., Laio, A.: Estimating the intrinsic dimension of datasets by a minimal neighborhood info. Sci. Rep. (2017)","DOI":"10.1038\/s41598-017-11873-y"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Hill, B.M.: A simple general approach to inference about the tail of a distribution. 3(5), 1163\u20131174 (1975)","DOI":"10.1214\/aos\/1176343247"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Houle, M.E.: Dimensionality, discriminability, density and distance distributions. In: ICDMW13, pp. 468\u2013473 (2013)","DOI":"10.1109\/ICDMW.2013.139"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Houle, M.E.: Local intrinsic dimensionality I: an extreme-value-theoretic foundation for similarity applications. In: SISAP, pp. 64\u201379 (2017)","DOI":"10.1007\/978-3-319-68474-1_5"},{"key":"10_CR11","unstructured":"Huang, H., Campello, R.J.G.B., Erfani, S.M., Ma, X., Houle, M.E., Bailey, J.: LDReg: Local dimensionality regularized self-supervised learning. ICLR 24 . 10.48550\/arXiv.2401.10474"},{"issue":"28","key":"10_CR12","doi-asserted-by":"publisher","first-page":"3724","DOI":"10.1002\/sim.6728","volume":"34","author":"JG Ibrahim","year":"2015","unstructured":"Ibrahim, J.G., Chen, M., Gwon, Y., Chen, F.: The power prior: theory and applications. Stat. Med. 34(28), 3724\u20133749 (2015)","journal-title":"Stat. Med."},{"key":"10_CR13","doi-asserted-by":"publisher","unstructured":"James, W., Stein, C.: Estimation with quadratic loss (1992). https:\/\/doi.org\/10.1007\/978-1-4612-0919-5_30","DOI":"10.1007\/978-1-4612-0919-5_30"},{"issue":"1007","key":"10_CR14","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1098\/rspa.1946.0056","volume":"186","author":"H Jeffreys","year":"1946","unstructured":"Jeffreys, H.: An invariant form for the prior probability in estimation problems. Proc. R. Soc. Lond. A 186(1007), 453\u2013461 (1946)","journal-title":"Proc. R. Soc. Lond. A"},{"key":"10_CR15","unstructured":"Jolliffe, I.T.: Principal Component Analysis. Springer (2002)"},{"key":"10_CR16","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images (2009)"},{"key":"10_CR17","unstructured":"Levina, E., Bickel, P.J.: Maximum likelihood estimation of intrinsic dimension. In: NeurIPS (2004)"},{"key":"10_CR18","unstructured":"Ma, X., et al: Characterizing adversarial subspaces using local intrinsic dimensionality. In: ICLR (2018)"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Ma, X., Wang, Y., Houle, M.E., Zhou, S., Erfani, S.M., Xia, S., Wijewickrema, S.N.R., Bailey, J.: Dimensionality-driven learning with noisy labels. In: ICML (2018)","DOI":"10.1109\/CVPR.2018.00906"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Neyman, E., Roughgarden, T.: From proper scoring rules to max-min optimal forecast aggregation. In: EC \u201921, p.\u00a0734. ACM (2021)","DOI":"10.1145\/3465456.3467599"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Rozza, A., Lombardi, G., Ceruti, C., Casiraghi, E., Campadelli, P.: Novel high intrinsic dimensionality estimators. Mach. Learn. (2012)","DOI":"10.1007\/s10994-012-5294-7"},{"key":"10_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2022.101989","volume":"108","author":"E Thordsen","year":"2022","unstructured":"Thordsen, E., Schubert, E.: ABID: angle based intrinsic dimensionality-theory and analysis. Inf. Syst. 108, 101989 (2022)","journal-title":"Inf. Syst."},{"key":"10_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s10035-022-01233-7","volume-title":"A representation learning framework for detection and characterization of dead versus strain localization zones from pre-to post-failure","author":"A Tordesillas","year":"2022","unstructured":"Tordesillas, A., Zhou, S., Bailey, J., Bondell, H.: A representation learning framework for detection and characterization of dead versus strain localization zones from pre-to post-failure. Gra, Mat (2022)"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"10_CR25","doi-asserted-by":"publisher","unstructured":"Zhou, S., Tordesillas, A., Pouragha, M., Bailey, J., Bondell, H.: On local intrinsic dimensionality of deformation in complex materials. Nat. Sci. Rep. 11(10216) (2021). https:\/\/doi.org\/10.1038\/s41598-021-89328-8","DOI":"10.1038\/s41598-021-89328-8"}],"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\/978-3-031-75823-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T17:05:32Z","timestamp":1745427932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-75823-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"ISBN":["9783031758225","9783031758232"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-75823-2_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,25]]},"assertion":[{"value":"25 October 2024","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":"Providence, RI","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sisap2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.sisap.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}