{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:30:26Z","timestamp":1771065026584,"version":"3.50.1"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:p>\n            Anchor graph hashing is used in many applications such as cancer detection, web page classification, and drug discovery. It computes the hash codes from the eigenvectors of the matrix representing the similarities between data points and anchor points; anchors refer to the points representing the data distribution. In performing an approximate nearest neighbor search, the hash codes of a query data point are determined by identifying its closest anchor points. Anchor graph hashing, however, incurs high computation cost since (1) the computation cost of obtaining the eigenvectors is quadratic to the number of anchor points, and (2) the similarities of the query data point to all the anchor points must be computed. Our proposal,\n            <jats:italic>Tridiagonal hashing<\/jats:italic>\n            , increases the efficiency of anchor graph hashing because of its two advances: (1) we apply a graph clustering algorithm to compute the eigenvectors from the tridiagonal matrix obtained from the similarities between data points and anchor points, and (2) we detect anchor points closest to the query data point by using a dimensionality reduction approach. Experiments show that our approach is several orders of magnitude faster than the previous approaches. Besides, it yields high search accuracy than the original anchor graph hashing approach.\n          <\/jats:p>","DOI":"10.14778\/3447689.3447696","type":"journal-article","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T16:20:06Z","timestamp":1618244406000},"page":"916-928","source":"Crossref","is-referenced-by-count":5,"title":["Fast algorithm for anchor graph hashing"],"prefix":"10.14778","volume":"14","author":[{"given":"Yasuhiro","family":"Fujiwara","sequence":"first","affiliation":[{"name":"NTT Communication Science"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sekitoshi","family":"Kanai","sequence":"additional","affiliation":[{"name":"Keio University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasutoshi","family":"Ida","sequence":"additional","affiliation":[{"name":"NTT Software Innovation Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atsutoshi","family":"Kumagai","sequence":"additional","affiliation":[{"name":"NTT Software Innovation Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naonori","family":"Ueda","sequence":"additional","affiliation":[{"name":"NTT Communication Science"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/3281337"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/98524.98564"},{"key":"e_1_2_1_3_1","volume-title":"Carreira-Perpi\u00f1\u00e1n and Ramin Raziperchikolaei","author":"Miguel","year":"2015","unstructured":"Miguel \u00c1. Carreira-Perpi\u00f1\u00e1n and Ramin Raziperchikolaei . 2015 . Hashing with Binary Autoencoders. In CVPR. 557--566. Miguel \u00c1. Carreira-Perpi\u00f1\u00e1n and Ramin Raziperchikolaei. 2015. Hashing with Binary Autoencoders. In CVPR. 557--566."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/846219.847295"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2012.06.003"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/872757.872795"},{"key":"e_1_2_1_7_1","volume-title":"Numerical Linear Algebra with Applications: Using MATLAB","author":"Ford William","unstructured":"William Ford . 2014. Numerical Linear Algebra with Applications: Using MATLAB . Academic Press . William Ford. 2014. Numerical Linear Algebra with Applications: Using MATLAB. Academic Press."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/3021924.3021938"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735496.2735498"},{"key":"e_1_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Yasuhiro Fujiwara Atsutoshi Kumagai Sekitoshi Kanai Yasutoshi Ida and Naonori Ueda. 2020. Efficient Algorithm for the b-Matching Graph. In KDD. 187--197.  Yasuhiro Fujiwara Atsutoshi Kumagai Sekitoshi Kanai Yasutoshi Ida and Naonori Ueda. 2020. Efficient Algorithm for the b-Matching Graph. In KDD. 187--197.","DOI":"10.1145\/3394486.3403061"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783280"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2213836.2213898"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.5555\/645925.671516"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2652730"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/971697.602266"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/43.159993"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1137\/090771806"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.4086\/toc.2012.v008a014"},{"key":"e_1_2_1_19_1","volume-title":"Matrix Algebra From a Statistician's Perspective","author":"Harville David A.","unstructured":"David A. Harville . 2008. Matrix Algebra From a Statistician's Perspective . Springer . David A. Harville. 2008. Matrix Algebra From a Statistician's Perspective. Springer."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2005.66"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2875312"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.14778\/2850469.2850470"},{"key":"e_1_2_1_23_1","unstructured":"Yasutoshi Ida Yasuhiro Fujiwara and Hisashi Kashima. 2019. Fast Sparse Group Lasso. In NeurIPS. 1700--1708.  Yasutoshi Ida Yasuhiro Fujiwara and Hisashi Kashima. 2019. Fast Sparse Group Lasso. In NeurIPS. 1700--1708."},{"key":"e_1_2_1_24_1","unstructured":"Yasutoshi Ida Sekitoshi Kanai Yasuhiro Fujiwara Tomoharu Iwata Koh Takeuchi and Hisashi Kashima. 2020. Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance. In ICML. 4594--4603.  Yasutoshi Ida Sekitoshi Kanai Yasuhiro Fujiwara Tomoharu Iwata Koh Takeuchi and Hisashi Kashima. 2020. Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance. In ICML. 4594--4603."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0036144598334138"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/253262.253347"},{"key":"e_1_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Brian Kulis and Kristen Grauman. 2009. Kernelized Locality-sensitive Hashing for Scalable Image Search. In ICCV. 2130--2137.  Brian Kulis and Kristen Grauman. 2009. Kernelized Locality-sensitive Hashing for Scalable Image Search. In ICCV. 2130--2137.","DOI":"10.1109\/ICCV.2009.5459466"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2002.1019214"},{"key":"e_1_2_1_29_1","unstructured":"Mingjie Li Ying Zhang Yifang Sun Wei Wang Ivor W. Tsang and Xuemin Lin. 2020. I\/O Efficient Approximate Nearest Neighbour Search based on Learned Functions. In ICDE. 289--300.  Mingjie Li Ying Zhang Yifang Sun Wei Wang Ivor W. Tsang and Xuemin Lin. 2020. I\/O Efficient Approximate Nearest Neighbour Search based on Learned Functions. In ICDE. 289--300."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2909204"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298483.3298557"},{"key":"e_1_2_1_32_1","volume-title":"Metaxas","author":"Liu Jingjing","year":"2014","unstructured":"Jingjing Liu , Shaoting Zhang , Wei Liu , Xiaofan Zhang , and Dimitris N . Metaxas . 2014 . Scalable Mammogram Retrieval Using Anchor Graph Hashing. In ISBI. 898--901. Jingjing Liu, Shaoting Zhang, Wei Liu, Xiaofan Zhang, and Dimitris N. Metaxas. 2014. Scalable Mammogram Retrieval Using Anchor Graph Hashing. In ISBI. 898--901."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104409"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969208"},{"key":"e_1_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Wanqi Liu Hanchen Wang Ying Zhang Wei Wang and Lu Qin. 2019. I-LSH: I\/O Efficient c-Approximate Nearest Neighbor Search in High-Dimensional Space. In ICDE. 1670--1673.  Wanqi Liu Hanchen Wang Ying Zhang Wei Wang and Lu Qin. 2019. I-LSH: I\/O Efficient c-Approximate Nearest Neighbor Search in High-Dimensional Space. In ICDE. 1670--1673.","DOI":"10.1109\/ICDE.2019.00169"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104482.3104483"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.14778\/3397230.3397240"},{"key":"e_1_2_1_38_1","volume-title":"Fast Algorithm for Detecting Community Structure in Networks. Physical Review E 69","author":"Newman M.E.J.","year":"2003","unstructured":"M.E.J. Newman . 2003. Fast Algorithm for Detecting Community Structure in Networks. Physical Review E 69 ( 2003 ). M.E.J. Newman. 2003. Fast Algorithm for Detecting Community Structure in Networks. Physical Review E 69 (2003)."},{"key":"e_1_2_1_39_1","volume-title":"A Generalized Power Iteration Method for Solving Quadratic Problem on the Stiefel Manifold. Sci. China Inf. Sci. 60, 11","author":"Nie Feiping","year":"2017","unstructured":"Feiping Nie , Rui Zhang , and Xuelong Li. 2017. A Generalized Power Iteration Method for Solving Quadratic Problem on the Stiefel Manifold. Sci. China Inf. Sci. 60, 11 ( 2017 ), 112101:1--112101:10. Feiping Nie, Rui Zhang, and Xuelong Li. 2017. A Generalized Power Iteration Method for Solving Quadratic Problem on the Stiefel Manifold. Sci. China Inf. Sci. 60, 11 (2017), 112101:1--112101:10."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/369133.369228"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.5555\/1403886"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330875"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390263"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.5555\/829576"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.5555\/1007752"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735461.2735462"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/1559845.1559905"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40994-3_17"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045414"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-007-9033-z"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.5555\/645924.671192"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.5555\/2981780.2981999"},{"key":"e_1_2_1_53_1","doi-asserted-by":"crossref","unstructured":"Yi Zheng Hui Peng Xiaocai Zhang Xiaoying Gao and Jinyan Li. 2018. Predicting Drug Targets from Heterogeneous Spaces using Anchor Graph Hashing and Ensemble Learning. In IJCNN. 1--7.  Yi Zheng Hui Peng Xiaocai Zhang Xiaoying Gao and Jinyan Li. 2018. Predicting Drug Targets from Heterogeneous Spaces using Anchor Graph Hashing and Ensemble Learning. In IJCNN. 1--7.","DOI":"10.1109\/IJCNN.2018.8489028"},{"key":"e_1_2_1_54_1","volume-title":"LWCS: A Large-scale Web Page Classification System Based on Anchor Graph Hashing. In ICSESS. 90--94.","author":"Zheng Y.","year":"2015","unstructured":"Y. Zheng , C. Sun , C. Zhu , X. Lan , X. Fu , and W. Han . 2015 . LWCS: A Large-scale Web Page Classification System Based on Anchor Graph Hashing. In ICSESS. 90--94. Y. Zheng, C. Sun, C. Zhu, X. Lan, X. Fu, and W. Han. 2015. LWCS: A Large-scale Web Page Classification System Based on Anchor Graph Hashing. In ICSESS. 90--94."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3447689.3447696","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:17:43Z","timestamp":1672226263000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3447689.3447696"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2]]},"references-count":54,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["10.14778\/3447689.3447696"],"URL":"https:\/\/doi.org\/10.14778\/3447689.3447696","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2021,2]]}}}