|Hashing for Distributed Data|
|Leng, Cong; Wu, Jiaxiang; Cheng, Jian; Zhang, Xi; Lu, Hanqing|
|会议名称||International Conference on Machine Learning|
|会议录名称||International Conference on Machine Learning|
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.
|Leng, Cong,Wu, Jiaxiang,Cheng, Jian,et al. Hashing for Distributed Data[C],2015.|
|ICML2015_Hashing for（340KB）||会议论文||开放获取||CC BY-NC-SA||浏览 下载|