{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T06:53:36Z","timestamp":1725605616601},"reference-count":38,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1109\/bigdata47090.2019.9006431","type":"proceedings-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T06:05:34Z","timestamp":1582610734000},"page":"46-55","source":"Crossref","is-referenced-by-count":0,"title":["Finding Stable Clustering for Noisy Data via Structure-Aware Representation"],"prefix":"10.1109","author":[{"given":"Huiyuan","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/BF01647331"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2356471"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972757.70"},{"key":"ref31","article-title":"Lazysvd: Even faster svd decomposition yet without agonizing pain","author":"allen-zhu","year":"2016","journal-title":"NeurIPS"},{"key":"ref30","article-title":"Fast stochastic algorithms for svd and pca: Convergence properties and convexity","author":"shamir","year":"2016","journal-title":"ICML"},{"key":"ref37","article-title":"Semi-supervised learning via compact latent space clustering","author":"kamnitsas","year":"2018","journal-title":"ICML"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/860435.860485"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.1262185"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2553459"},{"key":"ref10","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v30i1.10302","article-title":"The constrained laplacian rank algorithm for graph-based clustering","author":"nie","year":"2016","journal-title":"AAAI"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623726"},{"key":"ref12","article-title":"Regularized spectral learning","author":"meila","year":"2005","journal-title":"AISTATS"},{"key":"ref13","article-title":"Self-tuning spectral clustering","author":"zelnik-manor","year":"2005","journal-title":"NeurIPS"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.209"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339736"},{"key":"ref16","article-title":"Clustering by low-rank doubly stochastic matrix decomposition","author":"yang","year":"2012","journal-title":"ICML"},{"key":"ref17","article-title":"Low-rank riemannian optimization on positive semidefinite stochastic matrices with applications to graph clustering","author":"douik","year":"2018","journal-title":"ICML"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-016-0998-9"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2011.15"},{"key":"ref28","article-title":"Random features for large-scale kernel machines","author":"rahimi","year":"2008","journal-title":"NeurIPS"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v28i1.8950","article-title":"Robust multi-view spectral clustering via low-rank and sparse decomposition","author":"xia","year":"2014","journal-title":"AAAI"},{"key":"ref27","article-title":"Randomized block krylov methods for stronger and faster approximate singular value decomposition","author":"musco","year":"2015","journal-title":"NeurIPS"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3307339.3342142"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.188"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939794"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098156"},{"key":"ref8","article-title":"Learning doubly stochastic affinity matrix via daviskahan theorem","author":"park","year":"2017","journal-title":"ICDM"},{"key":"ref7","article-title":"Doubly stochastic normalization for spectral clustering","author":"zass","year":"2007","journal-title":"NeurIPS"},{"key":"ref2","article-title":"On spectral clustering: Analysis and an algorithm","author":"ng","year":"2002","journal-title":"NeurIPS"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939805"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-007-9033-z"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220090"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/1970392.1970395"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271740"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.35.11.652"},{"journal-title":"Spectral Graph Theory","year":"1997","author":"chung","key":"ref23"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-012-0584-1"},{"key":"ref25","article-title":"The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices","author":"lin","year":"2010","journal-title":"Arxiv preprint arXiv 1009 5055"}],"event":{"name":"2019 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2019,12,9]]},"location":"Los Angeles, CA, USA","end":{"date-parts":[[2019,12,12]]}},"container-title":["2019 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8986695\/9005444\/09006431.pdf?arnumber=9006431","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T12:23:43Z","timestamp":1665923023000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9006431\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/bigdata47090.2019.9006431","relation":{},"subject":[],"published":{"date-parts":[[2019,12]]}}}