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一种基于学习的高维数据c-近似最近邻查询算法
引用本文:袁培森,沙朝锋,王晓玲,周傲英.一种基于学习的高维数据c-近似最近邻查询算法[J].软件学报,2012,23(8):2018-2031.
作者姓名:袁培森  沙朝锋  王晓玲  周傲英
作者单位:1. 上海市智能信息处理重点实验室(复旦大学),上海,200433
2. 上海市高可信计算重点实验室(华东师范大学),上海,200062
基金项目:国家自然科学基金,国家重点基础研究发展计划(973),“核心电子器件、高端通用芯片及基础软件产品”国家科技重大专项
摘    要:针对高维数据近似最近邻查询,在过滤-验证框架下提出了一种基于学习的数据相关的c-近似最近邻查询算法.证明了数据经过随机投影之后,满足语义哈希技术所需的熵最大化准则.把经过随机投影的二进制数据作为数据的类标号,训练一组分类器用来预测查询的类标号.在此基础上,计算查询与数据集中数据对象的海明距离.最后,在过滤后的候选数据集上计算查询的最近邻与现有方法相比,该方法对空间需求更小,编码长度更短,效率更高.模拟数据集和真实数据集上的实验结果表明,该方法不仅能够提高查询效率,而且方便调控在查询质量和查询处理时间方面的平衡问题.

关 键 词:随机投影  c-近似最近邻查询  支持向量机分类器  高维数据  熵最大化准则  位置敏感哈希
收稿时间:2011/1/24 0:00:00
修稿时间:2011/4/28 0:00:00

c-Approximate Nearest Neighbor Query Algorithm Based on Learning for High-Dimensional Data
YUAN Pei-Sen,SHA Chao-Feng,WANG Xiao-Ling and ZHOU Ao-Ying.c-Approximate Nearest Neighbor Query Algorithm Based on Learning for High-Dimensional Data[J].Journal of Software,2012,23(8):2018-2031.
Authors:YUAN Pei-Sen  SHA Chao-Feng  WANG Xiao-Ling and ZHOU Ao-Ying
Affiliation:1(Shanghai Key Laboratory of Intelligent Information Processing(Fudan University),Shanghai 200433,China) 2(Shanghai Key Laboratory of Trustworthy Computing(East China Normal University),Shanghai 200062,China)
Abstract:Under the filter-and-refine framework and based on the learning techniques,a data-aware method for c-approximate nearest neighbor query for high-dimensional data is proposed in this paper.The study claims that data after random projection satisfies the entropy-maximizing criterion which is needed by the semantic hashing. The binary codes after random projection are treated as the labels,and a group of classifiers are trained,which are used for predicting the binary code for the query.The data objects are selected who’s Hamming distances between the query satisfying the threshold as the candidates.The real distances are evaluated on the candidate subset and the smallest one is returned.Experimental results on the synthetic datasets and the real datasets show that this method outperforms the existing work with shorter binary code,in addition,the performance and the result quality can be easily tuned.
Keywords:random projection  c-approximate nearest neighbor query  SVM classifier  high-dimensional data  entropy maximizing criterion  locality sensitive hashing
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