{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:25:10Z","timestamp":1750220710195,"version":"3.41.0"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T00:00:00Z","timestamp":1584057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF: RAISE: CA-FW-HTF: Prepare the US Labor Force for Future Jobs in the Hotel and Restaurant Industry: A Hybrid Framework and Multi-Stakeholder Approach","award":["1937833"],"award-info":[{"award-number":["1937833"]}]},{"name":"NSF: CRII: CSR: NeuroMC---Parallel Online Scheduling of Mixed-Criticality Real-Time Systems via Neural Networks","award":["1755965"],"award-info":[{"award-number":["1755965"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2020,6,30]]},"abstract":"<jats:p>Sparse Discriminant Analysis (SDA) has been widely used to improve the performance of classical Fisher\u2019s Linear Discriminant Analysis in supervised metric learning, feature selection, and classification. With the increasing needs of distributed data collection, storage, and processing, enabling the Sparse Discriminant Learning to embrace the multi-party distributed computing environments becomes an emerging research topic. This article proposes a novel multi-party SDA algorithm, which can learn SDA models effectively without sharing any raw data and basic statistics among machines. The proposed algorithm (1) leverages the direct estimation of SDA to derive a distributed loss function for the discriminant learning, (2) parameterizes the distributed loss function with local\/global estimates through bootstrapping, and (3) approximates a global estimation of linear discriminant projection vector by optimizing the \u201cdistributed bootstrapping loss function\u201d with gossip-based stochastic gradient descent. Experimental results on both synthetic and real-world benchmark datasets show that our algorithm can compete with the aggregated SDA with similar performance, and significantly outperforms the most recent distributed SDA in terms of accuracy and F1-score.<\/jats:p>","DOI":"10.1145\/3374919","type":"journal-article","created":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T14:48:38Z","timestamp":1584110918000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["MP\n            <sup>2<\/sup>\n            SDA"],"prefix":"10.1145","volume":"14","author":[{"given":"Jiang","family":"Bian","sequence":"first","affiliation":[{"name":"University of Central Florida, Orlando, Florida"}]},{"given":"Haoyi","family":"Xiong","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}]},{"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[{"name":"Missouri University of Science and Technology, Rolla, Missouri"}]},{"given":"Jun","family":"Huan","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5967-1058","authenticated-orcid":false,"given":"Zhishan","family":"Guo","sequence":"additional","affiliation":[{"name":"University of Central Florida, Orlando, Florida"}]}],"member":"320","published-online":{"date-parts":[[2020,3,13]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/10720076_29"},{"volume-title":"Statistical Decision Theory and Bayesian Analysis","author":"Berger James O.","key":"e_1_2_1_2_1","unstructured":"James O. Berger . 2013. Statistical Decision Theory and Bayesian Analysis . Springer Science 8 Business Media. James O. Berger. 2013. Statistical Decision Theory and Bayesian Analysis. Springer Science 8 Business Media."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/1388350.1388354"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/BHI.2017.7897304"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.86"},{"volume-title":"32nd AAAI Conference on Artificial Intelligence.","author":"Bian Jiang","key":"e_1_2_1_6_1","unstructured":"Jiang Bian , Haoyi Xiong , Yanjie Fu , and Sajal K. Das . 2018. CSWA: Aggregation-free spatial-temporal community sensing . In 32nd AAAI Conference on Artificial Intelligence. Jiang Bian, Haoyi Xiong, Yanjie Fu, and Sajal K. Das. 2018. CSWA: Aggregation-free spatial-temporal community sensing. In 32nd AAAI Conference on Artificial Intelligence."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10207-012-0177-2"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2011.tm11199"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1214\/15-EJS1081"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/11818175_31"},{"key":"e_1_2_1_11_1","volume-title":"International Joint Conferences on Artificial Intelligence. 3409--3415","author":"Jian Lou Cheung","year":"2015","unstructured":"Yiu-ming Cheung and Jian Lou . 2015 . Efficient generalized conditional gradient with gradient sliding for composite optimization . In International Joint Conferences on Artificial Intelligence. 3409--3415 . Yiu-ming Cheung and Jian Lou. 2015. Efficient generalized conditional gradient with gradient sliding for composite optimization. In International Joint Conferences on Artificial Intelligence. 3409--3415."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1198\/TECH.2011.08118"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0022"},{"key":"e_1_2_1_14_1","unstructured":"Jeffrey Dean Greg Corrado Rajat Monga Kai Chen Matthieu Devin Mark Mao Andrew Senior Paul Tucker Ke Yang Quoc V. Le etal 2012. Large scale distributed deep networks. In Advances in Neural Information Processing Systems. 1223--1231.  Jeffrey Dean Greg Corrado Rajat Monga Kai Chen Matthieu Devin Mark Mao Andrew Senior Paul Tucker Ke Yang Quoc V. Le et al. 2012. Large scale distributed deep networks. In Advances in Neural Information Processing Systems. 1223--1231."},{"key":"e_1_2_1_15_1","volume-title":"Stork","author":"Duda Richard O.","year":"2001","unstructured":"Richard O. Duda , Peter E. Hart , and David G . Stork . 2001 . Pattern Classification (2nd Ed). Wiley . Richard O. Duda, Peter E. Hart, and David G. Stork. 2001. Pattern Classification (2nd Ed). Wiley."},{"key":"e_1_2_1_16_1","volume-title":"Groetsch","author":"Engl Heinz W.","year":"2014","unstructured":"Heinz W. Engl and Charles W . Groetsch . 2014 . Inverse and Ill-posed Problems. Vol. 4 . Elsevier . Heinz W. Engl and Charles W. Groetsch. 2014. Inverse and Ill-posed Problems. Vol. 4. Elsevier."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006000"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxm045"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0028"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1215\/S0012-7094-48-01568-3"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1214\/15-EJS1031"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2697057"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.2307\/2986198"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.4135\/9781412983532"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176348649"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.2858"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1177013525"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1999.10474156"},{"volume-title":"Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods\u2014Support Vector Learning","author":"Platt John C.","key":"e_1_2_1_29_1","unstructured":"John C. Platt . 1999. Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods\u2014Support Vector Learning . MIT Press , Cambridge, MA , 185--208. John C. Platt. 1999. Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods\u2014Support Vector Learning. MIT Press, Cambridge, MA, 185--208."},{"key":"e_1_2_1_30_1","volume-title":"Annual Conference on Innovative Applications of Artificial Intelligence","volume":"1","author":"Foster","unstructured":"Foster J. Provost and Daniel N. Hennessy. 1996. Scaling up: Distributed machine learning with cooperation . In Annual Conference on Innovative Applications of Artificial Intelligence , Vol. 1 . 74--79. Foster J. Provost and Daniel N. Hennessy. 1996. Scaling up: Distributed machine learning with cooperation. In Annual Conference on Innovative Applications of Artificial Intelligence, Vol. 1. 74--79."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783390"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553482"},{"key":"e_1_2_1_33_1","volume-title":"AAAI Conference on Artificial Intelligence. 2000--2006","author":"Qian Hong","year":"2016","unstructured":"Hong Qian and Yang Yu . 2016 . Scaling simultaneous optimistic optimization for high-dimensional non-convex functions with low effective dimensions . In AAAI Conference on Artificial Intelligence. 2000--2006 . Hong Qian and Yang Yu. 2016. Scaling simultaneous optimistic optimization for high-dimensional non-convex functions with low effective dimensions. In AAAI Conference on Artificial Intelligence. 2000--2006."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(98)00016-6"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1214\/10-AOS870"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2016.2607345"},{"key":"e_1_2_1_37_1","volume-title":"Asuncion","author":"Smyth Padhraic","year":"2009","unstructured":"Padhraic Smyth , Max Welling , and Arthur U . Asuncion . 2009 . Asynchronous distributed learning of topic models. In Advances in Neural Information Processing Systems . 81--88. Padhraic Smyth, Max Welling, and Arthur U. Asuncion. 2009. Asynchronous distributed learning of topic models. In Advances in Neural Information Processing Systems. 81--88."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176345632"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/1899412.1899418"},{"key":"e_1_2_1_40_1","volume-title":"Proceeding of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS\u201916)","author":"Tian Lu","year":"2016","unstructured":"Lu Tian and Quanquan Gu . 2016 . Communication-efficient distributed sparse linear discriminant analysis . In Proceeding of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS\u201916) . Lu Tian and Quanquan Gu. 2016. Communication-efficient distributed sparse linear discriminant analysis. In Proceeding of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS\u201916)."},{"volume-title":"Encyclopedia of Machine Learning","author":"Ting Kai Ming","key":"e_1_2_1_41_1","unstructured":"Kai Ming Ting . 2011. Precision and recall . In Encyclopedia of Machine Learning . Springer , 781--781. Kai Ming Ting. 2011. Precision and recall. In Encyclopedia of Machine Learning. Springer, 781--781."},{"volume-title":"50th Annual Allerton Conference on Communication, Control, and Computing (Allerton\u201912)","author":"Tsianos Konstantinos I.","key":"e_1_2_1_42_1","unstructured":"Konstantinos I. Tsianos , Sean Lawlor , and Michael G. Rabbat . 2012. Consensus-based distributed optimization: Practical issues and applications in large-scale machine learning . In 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton\u201912) . IEEE, 1543--1550. Konstantinos I. Tsianos, Sean Lawlor, and Michael G. Rabbat. 2012. Consensus-based distributed optimization: Practical issues and applications in large-scale machine learning. In 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton\u201912). IEEE, 1543--1550."},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2018.2799214"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1198\/jcgs.2011.11051a"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2009.00699.x"},{"key":"e_1_2_1_46_1","first-page":"905","article-title":"Personalized age progression with bi-level aging dictionary learning","volume":"40","author":"Li Zechao","year":"2017","unstructured":"Zechao Li , Hanjiang Lai , Liyan Zhang , Shuicheng Yan , Xiangbo Shu , and Jinhui Tang . 2017 . Personalized age progression with bi-level aging dictionary learning . IEEE Transactions on Pattern Analysis and Machine Intelligence 40 , 4 (2017), 905 -- 917 . Zechao Li, Hanjiang Lai, Liyan Zhang, Shuicheng Yan, Xiangbo Shu, and Jinhui Tang. 2017. Personalized age progression with bi-level aging dictionary learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 4 (2017), 905--917.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2015.2472014"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.ENG.2016.02.008"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2846783"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/401"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.62"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2017.2721541"},{"key":"e_1_2_1_53_1","volume-title":"2005 International Conference on Machine Learning and Cybernetics","volume":"3","author":"Zhang Jian-Pei","year":"2005","unstructured":"Jian-Pei Zhang , Zhong-Wei Li , and Jing Yang . 2005 . A parallel SVM training algorithm on large-scale classification problems . In 2005 International Conference on Machine Learning and Cybernetics , Vol. 3 . IEEE, 1637--1641. Jian-Pei Zhang, Zhong-Wei Li, and Jing Yang. 2005. A parallel SVM training algorithm on large-scale classification problems. In 2005 International Conference on Machine Learning and Cybernetics, Vol. 3. IEEE, 1637--1641."},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-68880-8_32"},{"key":"e_1_2_1_55_1","volume-title":"Smola","author":"Zinkevich Martin","year":"2010","unstructured":"Martin Zinkevich , Markus Weimer , Lihong Li , and Alex J . Smola . 2010 . Parallelized stochastic gradient descent. In Advances in Neural Information Processing Systems . 2595--2603. Martin Zinkevich, Markus Weimer, Lihong Li, and Alex J. Smola. 2010. Parallelized stochastic gradient descent. In Advances in Neural Information Processing Systems. 2595--2603."}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3374919","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3374919","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:33:09Z","timestamp":1750199589000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3374919"}},"subtitle":["Multi-Party Parallelized Sparse Discriminant Learning"],"short-title":[],"issued":{"date-parts":[[2020,3,13]]},"references-count":55,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,6,30]]}},"alternative-id":["10.1145\/3374919"],"URL":"https:\/\/doi.org\/10.1145\/3374919","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2020,3,13]]},"assertion":[{"value":"2019-03-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-12-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-03-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}