{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:57:31Z","timestamp":1760245051129},"reference-count":16,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2020,6,1]]},"DOI":"10.1587\/transinf.2019edp7182","type":"journal-article","created":{"date-parts":[[2020,5,31]],"date-time":"2020-05-31T22:09:46Z","timestamp":1590962986000},"page":"1355-1361","source":"Crossref","is-referenced-by-count":1,"title":["Partial Label Metric Learning Based on Statistical Inference"],"prefix":"10.1587","volume":"E103.D","author":[{"given":"Tian","family":"XIE","sequence":"first","affiliation":[{"name":"Information Engineering University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongchang","family":"CHEN","sequence":"additional","affiliation":[{"name":"China National Digital Switching System Engineering & Technological R&D Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuosiyu","family":"MING","sequence":"additional","affiliation":[{"name":"China National Digital Switching System Engineering & Technological R&D Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianpeng","family":"ZHANG","sequence":"additional","affiliation":[{"name":"China National Digital Switching System Engineering & Technological R&D Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"GAO","sequence":"additional","affiliation":[{"name":"China National Digital Switching System Engineering & Technological R&D Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaomei","family":"LI","sequence":"additional","affiliation":[{"name":"China National Digital Switching System Engineering & Technological R&D Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuehang","family":"DING","sequence":"additional","affiliation":[{"name":"China National Digital Switching System Engineering & Technological R&D Center"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] Z.H. Zhou, \u201cA brief introduction to weakly supervised learning,\u201d Natl. Sci. Rev., vol.5, no.2, pp.48-57, Jan. 2017.","DOI":"10.1093\/nsr\/nwx106"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] E. H\u00fcllermeier and J. Beringer, \u201cLearning from ambiguously labeled examples,\u201d Intell. Data Anal., vol.10, no.5, pp.419-439, Jan. 2006. 10.3233\/ida-2006-10503","DOI":"10.3233\/IDA-2006-10503"},{"key":"3","unstructured":"[3] M.L. Zhang and F. Yu, \u201cSolving the partial label learning problem: An instance-based approach,\u201d Proc. 24th Int. Joint Conf. Artif. Intell., Argentina, pp.4048-4054, 2015."},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] M.-L. Zhang, B.-B. Zhou, and X.-Y. Liu, \u201cPartial label learning via feature-aware disambiguation,\u201d Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. data Min., USA, pp.1335-1344, 2016. 10.1145\/2939672.2939788","DOI":"10.1145\/2939672.2939788"},{"key":"5","unstructured":"[5] J.B. Tenenbaum, \u201cA global geometric framework for nonlinear dimensionality reduction,\u201d Science, vol.290, no.5500, pp.2319-2323, 2000."},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] S.T. Roweis and L.K. Saul, \u201cNonlinear dimensionality reduction by locally linear embedding,\u201d Science, vol.290, no.5500, pp.2323-2326, 2000. 10.1126\/science.290.5500.2323","DOI":"10.1126\/science.290.5500.2323"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] B. Kulis, \u201cMetric learning: A survey,\u201d Found. and Trends in Mach. Learn., vol.5, no.4, pp.287-364, 2012. 10.1561\/2200000019","DOI":"10.1561\/2200000019"},{"key":"8","unstructured":"[8] K. Martin, M. Hirzer, P. Wohlhart, P.M. Roth, and H. Bischof, \u201cLarge scale metric learning from equivalence constraints,\u201d Proc. CVPR, USA, pp.2288-2295, 2012."},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] Y. Zhou and H. Gu, \u201cGeometric mean metric learning for partial label data,\u201d Neurocomputing, vol.275, pp.394-402, Aug. 2018. 10.1016\/j.neucom.2017.08.058","DOI":"10.1016\/j.neucom.2017.08.058"},{"key":"10","unstructured":"[10] T. Cour and B. Sapp, \u201cLearning from partial labels,\u201d J. Mach. Learn. Res., vol.12, no.2, pp.1501-1536, 2011."},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] M. Guillaumin, J. Verbeek, and C. Schmid, \u201cMultiple instance metric learning from automatically labeled bags of faces,\u201d Proc. ECCV, Greece, pp.634-647, 2010. 10.1007\/978-3-642-15549-9_46","DOI":"10.1007\/978-3-642-15549-9_46"},{"key":"12","unstructured":"[12] L.L. Liu and T.G. Dietterich, \u201cA conditional multinomial mixture model for superset label learning,\u201d Proc. Adv. Neural Inf. Process. Syst., USA, pp.557-565, 2012."},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] F. Briggs, X.Z. Fern, and R. Raich, \u201cRank-loss support instance machines for MIML instance annotation,\u201d Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discov. data Min., Beijing, China, pp.534-542, 2012. 10.1145\/2339530.2339616","DOI":"10.1145\/2339530.2339616"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] G. Panis and A.L. B, \u201cAn overview of research activities in facial age estimation using the FG-NET aging database,\u201d Proc. ECCV, Switzerland, pp.737-750, 2014.","DOI":"10.1007\/978-3-319-16181-5_56"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] M. Zhang, F. Yu, and C. Tang, \u201cDisambiguation-free partial label learning,\u201d IEEE Trans. Knowl. Data Eng., vol.29, no.10, pp.2155-2167, 2017. 10.1109\/tkde.2017.2721942","DOI":"10.1109\/TKDE.2017.2721942"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] D. Wang, L. Li, and M.-L. Zhang, \u201cAdaptive Graph Guided Disambiguation for Partial Label Learning,\u201d Proc. 25th ACM SIGKDD Conf. on Knowl. Discov. and Data Min., Anchorage, AK, pp.83-91, 2019. 10.1145\/3292500.3330840","DOI":"10.1145\/3292500.3330840"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E103.D\/6\/E103.D_2019EDP7182\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,6]],"date-time":"2020-06-06T03:25:59Z","timestamp":1591413959000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E103.D\/6\/E103.D_2019EDP7182\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,1]]},"references-count":16,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2020]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2019edp7182","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,1]]}}}