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Hashing for Distributed Data
Leng, Cong; Wu, Jiaxiang; Cheng, Jian; Zhang, Xi; Lu, Hanqing
Conference NameInternational Conference on Machine Learning
Source PublicationInternational Conference on Machine Learning
Conference Date2015-6
Conference PlaceFrance
Recently, hashing based approximate nearest neighbors search has attracted much attention. Extensive centralized hashing algorithms have been proposed and achieved promising performance. However, due to the large scale of many applications, the data is often stored or even collected in a distributed manner. Learning hash functions by aggregating all the data into a fusion center is infeasible because of the prohibitively
expensive communication and computation overhead. In this paper, we develop a novel hashing model to learn hash functions in a distributed setting. We cast a centralized hashing model as a set of subproblems with consensus constraints. We find these subproblems can be analytically solved in parallel on the distributed compute nodes. Since no training data is transmitted across the nodes in the learning process, the communication cost of our model is independent to the data size. Extensive experiments on several large scale datasets containing up to 100 million samples demonstrate the efficacy of our method.
KeywordDistributed Hashing
Indexed ByEI
Document Type会议论文
Corresponding AuthorCheng, Jian
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Leng, Cong,Wu, Jiaxiang,Cheng, Jian,et al. Hashing for Distributed Data[C],2015.
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