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    • 1. 发明申请
    • METHOD AND SYSTEM FOR FAST SIMILARITY COMPUTATION IN HIGH DIMENSIONAL SPACE
    • 用于在高维空间中快速相似计算的方法和系统
    • US20130031059A1
    • 2013-01-31
    • US13189696
    • 2011-07-25
    • Shanmugasundaram RavikumarAnirban DasguptaTamas Sarlos
    • Shanmugasundaram RavikumarAnirban DasguptaTamas Sarlos
    • G06F17/30
    • G06F17/30628
    • Method, system, and programs for computing similarity. Input data is first received from one or more data sources and then analyzed to obtain an input feature vector that characterizes the input data. An index is then generated based on the input feature vector and is used to archive the input data, where the value of the index is computed based on an improved Johnson-Lindenstrass transformation (FJLT) process. With the improved FJLT process, first, the sign of each feature in the input feature vector is randomly flipped to obtain a flipped vector. A Hadamard transformation is then applied to the flipped vector to obtain a transformed vector. An inner product between the transformed vector and a sparse vector is then computed to obtain a base vector, based on which the value of the index is determined.
    • 用于计算相似度的方法,系统和程序。 首先从一个或多个数据源接收输入数据,然后分析以获得表征输入数据的输入特征向量。 然后基于输入特征向量生成索引,并且用于存档输入数据,其中基于改进的约翰逊 - 林登斯特拉斯变换(FJLT)处理来计算索引的值。 随着改进的FJLT过程,首先,输入特征向量中的每个特征的符号被随机翻转以获得翻转矢量。 然后将Hadamard变换应用于翻转矢量以获得变换矢量。 然后计算变换向量和稀疏向量之间的内积,以获得基准向量,基于此确定索引的值。
    • 2. 发明授权
    • Method and system for fast similarity computation in high dimensional space
    • 高维空间快速相似度计算方法与系统
    • US08515964B2
    • 2013-08-20
    • US13189696
    • 2011-07-25
    • Shanmugasundaram RavikumarAnirban DasguptaTamas Sarlos
    • Shanmugasundaram RavikumarAnirban DasguptaTamas Sarlos
    • G06F17/30
    • G06F17/30628
    • Method, system, and programs for computing similarity. Input data is first received from one or more data sources and then analyzed to obtain an input feature vector that characterizes the input data. An index is then generated based on the input feature vector and is used to archive the input data, where the value of the index is computed based on an improved Johnson-Lindenstrass transformation (FJLT) process. With the improved FJLT process, first, the sign of each feature in the input feature vector is randomly flipped to obtain a flipped vector. A Hadamard transformation is then applied to the flipped vector to obtain a transformed vector. An inner product between the transformed vector and a sparse vector is then computed to obtain a base vector, based on which the value of the index is determined.
    • 用于计算相似度的方法,系统和程序。 首先从一个或多个数据源接收输入数据,然后分析以获得表征输入数据的输入特征向量。 然后基于输入特征向量生成索引,并且用于存档输入数据,其中基于改进的约翰逊 - 林登斯特拉斯变换(FJLT)处理来计算索引的值。 随着改进的FJLT过程,首先,输入特征向量中的每个特征的符号被随机翻转以获得翻转矢量。 然后将Hadamard变换应用于翻转矢量以获得变换矢量。 然后计算变换向量和稀疏向量之间的内积,以获得基准向量,基于此确定索引的值。