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Method for constructing covariance matrices from data features

机译:从数据特征构造协方差矩阵的方法

摘要

A method constructs descriptors for a set of data samples and determines a distance score between pairs of subsets selected from the set of data samples. A d-dimensional feature vector is extracted for each sample in each subset of samples. The feature vector includes indices to the corresponding sample and properties of the sample. The feature vectors of each subset of samples are combined into a d×d dimensional covariance matrix. The covariance matrix is a descriptor of the corresponding subset of samples. Then, a distance score is determined between the two subsets of samples using the descriptors to measure a similarity between the descriptors.
机译:一种方法构造用于一组数据样本的描述符,并确定从该组数据样本中选择的子集对之间的距离得分。为样本的每个子集中的每个样本提取d维特征向量。特征向量包括对应样本的索引和样本的属性。样本的每个子集的特征向量都组合成d×d维协方差矩阵。协方差矩阵是相应样本子集的描述符。然后,使用描述符来测量样本的两个子集之间的距离得分,以测量描述符之间的相似性。

著录项

  • 公开/公告号US7720289B2

    专利类型

  • 公开/公告日2010-05-18

    原文格式PDF

  • 申请/专利权人 FATIH M. PORIKLI;ONCEL TUZEL;

    申请/专利号US20050305427

  • 发明设计人 ONCEL TUZEL;FATIH M. PORIKLI;

    申请日2005-12-14

  • 分类号G06K9/46;G06K9/00;

  • 国家 US

  • 入库时间 2022-08-21 18:50:52

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