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Technol."],"published-print":{"date-parts":[[2018,9,30]]},"abstract":"<jats:p>Hashing techniques have recently gained increasing research interest in multimedia studies. Most existing hashing methods only employ single features for hash code learning. Multiview data with each view corresponding to a type of feature generally provides more comprehensive information. How to efficiently integrate multiple views for learning compact hash codes still remains challenging. In this article, we propose a novel unsupervised hashing method, dubbed multiview discrete hashing (MvDH), by effectively exploring multiview data. Specifically, MvDH performs matrix factorization to generate the hash codes as the latent representations shared by multiple views, during which spectral clustering is performed simultaneously. The joint learning of hash codes and cluster labels enables that MvDH can generate more discriminative hash codes, which are optimal for classification. An efficient alternating algorithm is developed to solve the proposed optimization problem with guaranteed convergence and low computational complexity. The binary codes are optimized via the discrete cyclic coordinate descent (DCC) method to reduce the quantization errors. Extensive experimental results on three large-scale benchmark datasets demonstrate the superiority of the proposed method over several state-of-the-art methods in terms of both accuracy and scalability.<\/jats:p>","DOI":"10.1145\/3178119","type":"journal-article","created":{"date-parts":[[2018,6,4]],"date-time":"2018-06-04T13:41:34Z","timestamp":1528119694000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":80,"title":["Multiview Discrete Hashing for Scalable Multimedia Search"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8494-4532","authenticated-orcid":false,"given":"Xiaobo","family":"Shen","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fumin","family":"Shen","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[{"name":"Northumbria University, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun-Hao","family":"Yuan","sequence":"additional","affiliation":[{"name":"Yangzhou University, Yangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Liu","sequence":"additional","affiliation":[{"name":"The University of New South Wales, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan-Sen","family":"Sun","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,6]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"585","article-title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","volume":"14","author":"Belkin Mikhail","year":"2001","journal-title":"Proceedings of Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_2_1","volume-title":"Nonlinear Programming","author":"Bertsekas Dimitri P."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539928"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"5","author":"Chen Xinlei","year":"2011"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1646396.1646452"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.267"},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the International Conference on Very Large Data Bases. 518--529","author":"Gionis Aristides","year":"1999"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.193"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2527796"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2342533"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2544779"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33715-4_39"},{"key":"e_1_2_1_14_1","volume-title":"Learning Multiple Layers of Features from Tiny Images. 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