Hashing for Distributed Data
Leng, Cong; Wu, Jiaxiang; Cheng, Jian; Zhang, Xi; Lu, Hanqing
2015
会议名称International Conference on Machine Learning
会议录名称International Conference on Machine Learning
会议日期2015-6
会议地点France
摘要
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.
关键词Distributed Hashing
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11784
专题模式识别国家重点实验室_图像与视频分析
通讯作者Cheng, Jian
作者单位中科院自动化研究所
推荐引用方式
GB/T 7714
Leng, Cong,Wu, Jiaxiang,Cheng, Jian,et al. Hashing for Distributed Data[C],2015.
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