公开(公告)号：US20150117766A1

公开(公告)日：2015-04-30

申请号：US14459242

申请日：2014-08-13

Applicant: Raytheon BBN Technologies Corp.

Inventor： Manasvi Tickoo ,  Devansh Arpit ,  Xiaodan Zhuang ,  Walter Andrews ,  Pradeep Natarajan

IPC: G06K9/00 ,  G06N99/00 ,  G06K9/46

CPC classification number: G06N99/005 ,  G06K9/6235 ,  G06K2009/6236

Abstract: A method for feature transformation of a data set includes: receiving a data set including original feature samples with corresponding class labels; splitting the data set into a direction optimization set and a training set; using the direction optimization set to calculate an optimum transformation vector that maximizes inter-class separability and minimizes intra-class variance of the feature samples with respect to corresponding class labels; using the optimum transformation vector to transform the rest of the original feature samples of the data set to new feature samples with enhanced discriminative characteristics; and training a classifier using the new feature samples, wherein the method is performed by one or more processors.

Abstract translation: 一种用于数据集的特征变换的方法包括：接收包括具有相应类别标签的原始特征样本的数据集; 将数据集分为方向优化集和训练集; 使用方向优化集来计算最大化类间分离性并使特征样本相对于相应类标签的类内方差最小化的最佳变换向量; 使用最佳变换向量将数据集的其余原始特征样本变换为具有增强的鉴别特征的新特征样本; 并使用新的特征样本训练分类器，其中该方法由一个或多个处理器执行。

公开(公告)号：US09471886B2

公开(公告)日：2016-10-18

申请号：US14459242

申请日：2014-08-13

Applicant: Raytheon BBN Technologies Corp.

Inventor： Manasvi Tickoo ,  Devansh Arpit ,  Xiaodan Zhuang ,  Walter Andrews ,  Pradeep Natarajan

IPC: G06K9/00 ,  G06N99/00 ,  G06K9/46 ,  G06K9/62

CPC classification number: G06N99/005 ,  G06K9/6235 ,  G06K2009/6236

Abstract: A method for feature transformation of a data set includes: receiving a data set including original feature samples with corresponding class labels; splitting the data set into a direction optimization set and a training set; using the direction optimization set to calculate an optimum transformation vector that maximizes inter-class separability and minimizes intra-class variance of the feature samples with respect to corresponding class labels; using the optimum transformation vector to transform the rest of the original feature samples of the data set to new feature samples with enhanced discriminative characteristics; and training a classifier using the new feature samples, wherein the method is performed by one or more processors.

Abstract translation: 一种用于数据集的特征变换的方法包括：接收包括具有相应类别标签的原始特征样本的数据集; 将数据集分为方向优化集和训练集; 使用方向优化集来计算最大化类间分离性并使特征样本相对于相应类标签的类内方差最小化的最佳变换向量; 使用最佳变换向量将数据集的其余原始特征样本变换为具有增强的鉴别特征的新特征样本; 并使用新的特征样本训练分类器，其中该方法由一个或多个处理器执行。