{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:18:51Z","timestamp":1740158331576,"version":"3.37.3"},"reference-count":14,"publisher":"Wiley","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>Nonnegative matrix factorization (NMF) decomposes a high-dimensional nonnegative matrix into the product of two reduced dimensional nonnegative matrices. However, conventional NMF neither qualifies large-scale datasets as it maintains all data in memory nor preserves the geometrical structure of data which is needed in some practical tasks. In this paper, we propose a parallel NMF with manifold regularization method (PNMF-M) to overcome the aforementioned deficiencies by parallelizing the manifold regularized NMF on distributed computing system. In particular, PNMF-M distributes both data samples and factor matrices to multiple computing nodes instead of loading the whole dataset in a single node and updates both factor matrices locally on each node. In this way, PNMF-M succeeds to resolve the pressure of memory consumption for large-scale datasets and to speed up the computation by parallelization. For constructing the adjacency matrix in manifold regularization, we propose a two-step distributed graph construction method, which is proved to be equivalent to the batch construction method. Experimental results on popular text corpora and image datasets demonstrate that PNMF-M significantly improves both scalability and time efficiency of conventional NMF thanks to the parallelization on distributed computing system; meanwhile it significantly enhances the representation ability of conventional NMF thanks to the incorporated manifold regularization.<\/jats:p>","DOI":"10.1155\/2018\/6270816","type":"journal-article","created":{"date-parts":[[2018,5,2]],"date-time":"2018-05-02T19:37:21Z","timestamp":1525289841000},"page":"1-10","source":"Crossref","is-referenced-by-count":0,"title":["Parallel Nonnegative Matrix Factorization with Manifold Regularization"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8387-0831","authenticated-orcid":true,"given":"Fudong","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, Henan 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Shan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, Henan 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihang","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, Henan 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"doi-asserted-by":"publisher","key":"1","DOI":"10.1038\/44565"},{"year":"2003","key":"2"},{"doi-asserted-by":"publisher","key":"3","DOI":"10.1016\/j.ipm.2004.11.005"},{"doi-asserted-by":"publisher","key":"4","DOI":"10.1109\/TGRS.2006.888466"},{"doi-asserted-by":"publisher","key":"9","DOI":"10.1109\/LGRS.2008.2005793"},{"doi-asserted-by":"publisher","key":"11","DOI":"10.1007\/s10766-009-0116-7"},{"doi-asserted-by":"publisher","key":"12","DOI":"10.1109\/TPAMI.2010.231"},{"key":"13","first-page":"2399","volume":"7","year":"2006","journal-title":"Journal of Machine Learning Research"},{"doi-asserted-by":"publisher","key":"15","DOI":"10.1109\/TGRS.2012.2213825"},{"doi-asserted-by":"publisher","key":"16","DOI":"10.1016\/j.neucom.2015.07.150"},{"doi-asserted-by":"publisher","key":"18","DOI":"10.1016\/j.patcog.2008.09.002"},{"doi-asserted-by":"publisher","key":"19","DOI":"10.1155\/2014\/928051"},{"doi-asserted-by":"publisher","key":"21","DOI":"10.1109\/TNNLS.2016.2574748"},{"key":"22","first-page":"585","volume-title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","volume":"14","year":"2001"}],"container-title":["Journal of Electrical and Computer Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2018\/6270816.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2018\/6270816.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2018\/6270816.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,5,2]],"date-time":"2018-05-02T19:37:31Z","timestamp":1525289851000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/jece\/2018\/6270816\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":14,"alternative-id":["6270816","6270816"],"URL":"https:\/\/doi.org\/10.1155\/2018\/6270816","relation":{},"ISSN":["2090-0147","2090-0155"],"issn-type":[{"type":"print","value":"2090-0147"},{"type":"electronic","value":"2090-0155"}],"subject":[],"published":{"date-parts":[[2018]]}}}