{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T15:52:43Z","timestamp":1723218763613},"reference-count":17,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition Letters"],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1016\/j.patrec.2020.05.011","type":"journal-article","created":{"date-parts":[[2020,5,16]],"date-time":"2020-05-16T19:13:33Z","timestamp":1589656413000},"page":"425-430","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":30,"special_numbering":"C","title":["Parametric PCA for unsupervised metric learning"],"prefix":"10.1016","volume":"135","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-8253-2729","authenticated-orcid":false,"given":"Alexandre L.M.","family":"Levada","sequence":"first","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.patrec.2020.05.011_bib0001","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"3","key":"10.1016\/j.patrec.2020.05.011_bib0002","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1145\/502807.502808","article-title":"Searching in metric spaces","volume":"33","author":"Ch\u00e1vez","year":"2001","journal-title":"ACM Comput Surv"},{"issue":"10","key":"10.1016\/j.patrec.2020.05.011_bib0003","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1016\/j.camwa.2012.09.011","article-title":"Is the k-nn classifier in high dimensions affected by the curse of dimensionality?","volume":"65","author":"Pestov","year":"2013","journal-title":"Comput. Math. Appl."},{"key":"10.1016\/j.patrec.2020.05.011_bib0004","series-title":"Multivariate Density Estimation","author":"Scott","year":"1992"},{"issue":"10","key":"10.1016\/j.patrec.2020.05.011_bib0005","doi-asserted-by":"crossref","first-page":"2795","DOI":"10.1109\/78.324744","article-title":"Nonparametric multivariate density estimation: acomparative study","volume":"42","author":"Hwang","year":"1994","journal-title":"IEEE Trans.\u00a0 Signal Process."},{"key":"10.1016\/j.patrec.2020.05.011_bib0006","series-title":"Geometric Structure of High-Dimensional Data and Dimensionality Reduction","author":"Wang","year":"2012"},{"key":"10.1016\/j.patrec.2020.05.011_bib0007","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.neunet.2018.06.003","article-title":"Survey and experimental study on metric learning methods","volume":"105","author":"Li","year":"2018","journal-title":"Neural Netw."},{"key":"10.1016\/j.patrec.2020.05.011_bib0008","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.patcog.2016.08.025","article-title":"Joint sparse principal component analysis","volume":"61","author":"Yi","year":"2017","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patrec.2020.05.011_bib0009","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.patrec.2019.10.006","article-title":"A robust dimensionality reduction and matrix factorization framework for data clustering","volume":"128","author":"Li","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patrec.2020.05.011_bib0010","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.patrec.2019.05.009","article-title":"Similarity preservation in dimensionality reduction using a kernel-based cost function","volume":"125","author":"Garcia-Vega","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patrec.2020.05.011_bib0011","doi-asserted-by":"crossref","first-page":"107291","DOI":"10.1016\/j.patcog.2020.107291","article-title":"Novel dimensionality reduction approach for unsupervised learning on small datasets","volume":"103","author":"Hurtik","year":"2020","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patrec.2020.05.011_bib0012","series-title":"Nonlinear Dimensionality Reduction","author":"Lee","year":"2007"},{"issue":"8","key":"10.1016\/j.patrec.2020.05.011_bib0013","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1109\/JPROC.2018.2846730","article-title":"PCA in high dimensions: an orientation","volume":"106","author":"Johnstone","year":"2018","journal-title":"Proc.\u00a0 IEEE"},{"issue":"8","key":"10.1016\/j.patrec.2020.05.011_bib0014","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1109\/JPROC.2018.2853498","article-title":"Rethinking PCA for modern data sets: theory, algorithms, and applications","volume":"106","author":"Vaswani","year":"2018","journal-title":"Proc.\u00a0 IEEE"},{"key":"10.1016\/j.patrec.2020.05.011_bib0015","series-title":"Pattern Classification, 2nd Edition","author":"Duda","year":"2000"},{"issue":"9","key":"10.1016\/j.patrec.2020.05.011_bib0016","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/S0167-8655(99)00060-4","article-title":"Robust algorithms for principal component analysis","volume":"20","author":"Yang","year":"1999","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patrec.2020.05.011_bib0017","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: a graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comp.\u00a0 Appl. Math."}],"container-title":["Pattern Recognition Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167865520301835?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167865520301835?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T18:35:51Z","timestamp":1629484551000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167865520301835"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":17,"alternative-id":["S0167865520301835"],"URL":"http:\/\/dx.doi.org\/10.1016\/j.patrec.2020.05.011","relation":{},"ISSN":["0167-8655"],"issn-type":[{"value":"0167-8655","type":"print"}],"subject":[],"published":{"date-parts":[[2020,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Parametric PCA for unsupervised metric learning","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition Letters","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patrec.2020.05.011","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}
