{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:20:36Z","timestamp":1750306836459,"version":"3.41.0"},"reference-count":25,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2013,12,1]],"date-time":"2013-12-01T00:00:00Z","timestamp":1385856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91120303 and No. 61071181"],"award-info":[{"award-number":["91120303 and No. 61071181"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2013,12]]},"abstract":"<jats:p>\n            In this article, a novel framework based on trace norm minimization for audio classification is proposed. In this framework, both the feature extraction and classification are obtained by solving corresponding convex optimization problem with trace norm regularization. For feature extraction, robust principle component analysis (robust PCA) via minimization a combination of the nuclear norm and the \u2113\n            <jats:sub>1<\/jats:sub>\n            -norm is used to extract low-rank matrix features which are robust to white noise and gross corruption for audio signal. These low-rank matrix features are fed to a linear classifier where the weight and bias are learned by solving similar trace norm constrained problems. For this linear classifier, most methods find the parameters, that is the weight matrix and bias in batch-mode, which makes it inefficient for large scale problems. In this article, we propose a parallel online framework using accelerated proximal gradient method. This framework has advantages in processing speed and memory cost. In addition, as a result of the regularization formulation of matrix classification, the Lipschitz constant was given explicitly, and hence the step size estimation of the general proximal gradient method was omitted, and this part of computing burden is saved in our approach. Extensive experiments on real data sets for laugh\/non-laugh and applause\/non-applause classification indicate that this novel framework is effective and noise robust.\n          <\/jats:p>","DOI":"10.1145\/2542182.2542197","type":"journal-article","created":{"date-parts":[[2014,1,2]],"date-time":"2014-01-02T13:09:43Z","timestamp":1388668183000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Audio classification with low-rank matrix representation features"],"prefix":"10.1145","volume":"5","author":[{"given":"Ziqiang","family":"Shi","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiqing","family":"Han","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tieran","family":"Zheng","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2014,1,3]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_1_1_1","DOI":"10.1007\/s10994-007-5040-8"},{"volume-title":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. 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Arxiv preprint arXiv:1109.1342v1.","key":"e_1_2_1_18_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_19_1","DOI":"10.1109\/TMM.2007.911305"},{"key":"e_1_2_1_20_1","first-page":"1329","article-title":"Maximum-margin matrix factorization","volume":"17","author":"Srebro N.","year":"2005","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_21_1","first-page":"615","article-title":"An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems","volume":"6","author":"Toh K.","year":"2010","journal-title":"Pacific J. Optimization"},{"doi-asserted-by":"publisher","key":"e_1_2_1_22_1","DOI":"10.1145\/1273496.1273609"},{"doi-asserted-by":"publisher","key":"e_1_2_1_23_1","DOI":"10.1109\/TASL.2006.885921"},{"unstructured":"Wright J. Ganesh A. Rao S. and Ma Y. 2009. Robust principal component analysis&quest; In Proceedings of the Conference on Neural Information Processing Systems (NIPS'09).  Wright J. Ganesh A. 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Robust principal component analysis&quest; In Proceedings of the Conference on Neural Information Processing Systems (NIPS'09).","key":"e_1_2_1_24_1"},{"volume-title":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'08)","author":"Zhuang X.","key":"e_1_2_1_25_1"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2542182.2542197","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2542182.2542197","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T08:09:56Z","timestamp":1750234196000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2542182.2542197"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,12]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2013,12]]}},"alternative-id":["10.1145\/2542182.2542197"],"URL":"https:\/\/doi.org\/10.1145\/2542182.2542197","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"type":"print","value":"2157-6904"},{"type":"electronic","value":"2157-6912"}],"subject":[],"published":{"date-parts":[[2013,12]]},"assertion":[{"value":"2012-02-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2012-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2014-01-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}