公开(公告)号：US20090297046A1

公开(公告)日：2009-12-03

申请号：US12129515

申请日：2008-05-29

申请人： Deli Zhao ,  Zhouchen Lin ,  Rong Xiao ,  Xiaoou Tang

发明人： Deli Zhao ,  Zhouchen Lin ,  Rong Xiao ,  Xiaoou Tang

IPC分类号： G06K9/62

CPC分类号： G06K9/00275 ,  G06K9/6234

摘要： An exemplary method for extracting discriminant feature of samples includes providing data for samples in a multidimensional space; based on the data, computing local similarities for the samples; mapping the local similarities to weights; based on the mapping, formulating an inter-class scatter matrix and an intra-class scatter matrix; and based on the matrices, maximizing the ratio of inter-class scatter to intra-class scatter for the samples to provide discriminate features of the samples. Such a method may be used for classifying samples, recognizing patterns, or other tasks. Various other methods, devices, system, etc., are also disclosed.

摘要翻译： 用于提取样本的判别特征的示例性方法包括在多维空间中提供样本的数据; 基于数据，计算样本的局部相似度; 将局部相似性映射到权重; 基于映射，制定类间散布矩阵和类内散布矩阵; 并且基于矩阵，最大化样本之间的类间散射与类内散射的比率以提供样本的区别特征。 这种方法可用于分类样本，识别模式或其他任务。 还公开了各种其它方法，装置，系统等。

公开(公告)号：US08218880B2

公开(公告)日：2012-07-10

申请号：US12129515

申请日：2008-05-29

申请人： Deli Zhao ,  Zhouchen Lin ,  Rong Xiao ,  Xiaoou Tang

发明人： Deli Zhao ,  Zhouchen Lin ,  Rong Xiao ,  Xiaoou Tang

IPC分类号： G06K9/62 ,  G06K9/00 ,  G06K9/46

CPC分类号： G06K9/00275 ,  G06K9/6234

摘要： An exemplary method for extracting discriminant feature of samples includes providing data for samples in a multidimensional space; based on the data, computing local similarities for the samples; mapping the local similarities to weights; based on the mapping, formulating an inter-class scatter matrix and an intra-class scatter matrix; and based on the matrices, maximizing the ratio of inter-class scatter to intra-class scatter for the samples to provide discriminate features of the samples. Such a method may be used for classifying samples, recognizing patterns, or other tasks. Various other methods, devices, system, etc., are also disclosed.

摘要翻译： 用于提取样本的判别特征的示例性方法包括在多维空间中提供样本的数据; 基于数据，计算样本的局部相似度; 将局部相似性映射到权重; 基于映射，制定类间散布矩阵和类内散布矩阵; 并且基于矩阵，最大化样本之间的类间散射与类内散射的比率以提供样本的区别特征。 这种方法可用于分类样本，识别模式或其他任务。 还公开了各种其它方法，装置，系统等。

公开(公告)号：US20100080450A1

公开(公告)日：2010-04-01

申请号：US12242421

申请日：2008-09-30

申请人： Deli Zhao ,  Zhouchen Lin ,  Xiaoou Tang

发明人： Deli Zhao ,  Zhouchen Lin ,  Xiaoou Tang

IPC分类号： G06K9/62

CPC分类号： G06K9/6234 ,  G06K9/6252

摘要： Described is using semi-Riemannian geometry in supervised learning to learn a discriminant subspace for classification, e.g., labeled samples are used to learn the geometry of a semi-Riemannian submanifold. For a given sample, the K nearest classes of that sample are determined, along with the nearest samples that are in other classes, and the nearest samples in that sample's same class. The distances between these samples are computed, and used in computing a metric matrix. The metric matrix is used to compute a projection matrix that corresponds to the discriminant subspace. In online classification, as a new sample is received, it is projected into a feature space by use of the projection matrix and classified accordingly.

摘要翻译： 描述了在监督学习中使用半黎曼几何学习学习用于分类的判别子空间，例如，标记的样本用于学习半黎曼子流形歧管的几何形状。 对于给定的样本，该样本的K个最近类别以及其他类别中最近的样本以及该样本同一类中最近的样本进行确定。 计算这些样本之间的距离，并用于计算度量矩阵。 度量矩阵用于计算与判别子空间对应的投影矩阵。 在线分类中，作为收到的新样本，通过使用投影矩阵将其投影到特征空间中并进行分类。

公开(公告)号：US08064697B2

公开(公告)日：2011-11-22

申请号：US11871764

申请日：2007-10-12

申请人： Deli Zhao ,  Zhouchen Lin ,  Xiaoou Tang

发明人： Deli Zhao ,  Zhouchen Lin ,  Xiaoou Tang

IPC分类号： G06K9/00 ,  G06T7/00

CPC分类号： G06K9/6248

摘要： Systems and methods perform Laplacian Principal Components Analysis (LPCA). In one implementation, an exemplary system receives multidimensional data and reduces dimensionality of the data by locally optimizing a scatter of each local sample of the data. The optimization includes summing weighted distances between low dimensional representations of the data and a mean. The weights of the distances can be determined by a coding length of each local data sample. The system can globally align the locally optimized weighted scatters of the local samples and provide a global projection matrix. The LPCA improves performance of such applications as face recognition and manifold learning.

摘要翻译： 系统和方法执行拉普拉斯主成分分析（LPCA）。 在一个实现中，示例性系统通过局部优化数据的每个局部采样的散射来接收多维数据并且降低数据的维度。 优化包括对数据的低维表示和平均值之间的加权距离求和。 距离的权重可以通过每个本地数据样本的编码长度来确定。 该系统可以对局部采样的局部优化加权散射进行全局对齐，并提供全局投影矩阵。 LPCA可以改善诸如面部识别和歧管学习等应用的性能。

公开(公告)号：US07970727B2

公开(公告)日：2011-06-28

申请号：US12032705

申请日：2008-02-18

申请人： Deli Zhao ,  Zhouchen Lin ,  Xiaoou Tang

发明人： Deli Zhao ,  Zhouchen Lin ,  Xiaoou Tang

IPC分类号： G06F17/10

CPC分类号： G06K9/6248

摘要： A method for modeling data affinities and data structures. In one implementation, a contextual distance may be calculated between a selected data point in a data sample and a data point in a contextual set of the selected data point. The contextual set may include the selected data point and one or more data points in the neighborhood of the selected data point. The contextual distance may be the difference between the selected data point's contribution to the integrity of the geometric structure of the contextual set and the data point's contribution to the integrity of the geometric structure of the contextual set. The process may be repeated for each data point in the contextual set of the selected data point. The process may be repeated for each selected data point in the data sample. A digraph may be created using a plurality of contextual distances generated by the process.

摘要翻译： 一种用于建模数据亲和度和数据结构的方法。 在一个实现中，可以在数据样本中的所选数据点和所选数据点的上下文集合中的数据点之间计算上下文距离。 所述上下文集合可以包括所选数据点和所选数据点附近的一个或多个数据点。 上下文距离可以是所选数据点对上下文集合的几何结构的完整性的贡献与数据点对上下文集合的几何结构的完整性的贡献之间的差异。 可以对所选数据点的上下文集合中的每个数据点重复该过程。 可以对数据样本中的每个选定的数据点重复该过程。 可以使用由该过程生成的多个上下文距离来创建有向图。

公开(公告)号：US20090132213A1

公开(公告)日：2009-05-21

申请号：US12032705

申请日：2008-02-18

申请人： Deli Zhao ,  Zhouchen Lin ,  Xiaoou Tang

发明人： Deli Zhao ,  Zhouchen Lin ,  Xiaoou Tang

IPC分类号： G06F17/10

CPC分类号： G06K9/6248

摘要： A method for modeling data affinities and data structures. In one implementation, a contextual distance may be calculated between a selected data point in a data sample and a data point in a contextual set of the selected data point. The contextual set may include the selected data point and one or more data points in the neighborhood of the selected data point. The contextual distance may be the difference between the selected data point's contribution to the integrity of the geometric structure of the contextual set and the data point's contribution to the integrity of the geometric structure of the contextual set. The process may be repeated for each data point in the contextual set of the selected data point. The process may be repeated for each selected data point in the data sample. A digraph may be created using a plurality of contextual distances generated by the process.

摘要翻译： 一种用于建模数据亲和度和数据结构的方法。 在一个实现中，可以在数据样本中的所选数据点和所选数据点的上下文集合中的数据点之间计算上下文距离。 所述上下文集合可以包括所选数据点和所选数据点附近的一个或多个数据点。 上下文距离可以是所选数据点对上下文集合的几何结构的完整性的贡献与数据点对上下文集合的几何结构的完整性的贡献之间的差异。 可以对所选数据点的上下文集合中的每个数据点重复该过程。 可以对数据样本中的每个选定的数据点重复该过程。 可以使用由该过程生成的多个上下文距离来创建有向图。

公开(公告)号：US07996343B2

公开(公告)日：2011-08-09

申请号：US12242421

申请日：2008-09-30

申请人： Deli Zhao ,  Zhouchen Lin ,  Xiaoou Tang

发明人： Deli Zhao ,  Zhouchen Lin ,  Xiaoou Tang

IPC分类号： G06F11/00

CPC分类号： G06K9/6234 ,  G06K9/6252

摘要： Described is using semi-Riemannian geometry in supervised learning to learn a discriminant subspace for classification, e.g., labeled samples are used to learn the geometry of a semi-Riemannian submanifold. For a given sample, the K nearest classes of that sample are determined, along with the nearest samples that are in other classes, and the nearest samples in that sample's same class. The distances between these samples are computed, and used in computing a metric matrix. The metric matrix is used to compute a projection matrix that corresponds to the discriminant subspace. In online classification, as a new sample is received, it is projected into a feature space by use of the projection matrix and classified accordingly.

摘要翻译： 描述了在监督学习中使用半黎曼几何学习学习用于分类的判别子空间，例如，标记的样本用于学习半黎曼子流形歧管的几何形状。 对于给定的样本，该样本的K个最近类别以及其他类别中最近的样本以及该样本同一类中最近的样本进行确定。 计算这些样本之间的距离，并用于计算度量矩阵。 度量矩阵用于计算与判别子空间对应的投影矩阵。 在线分类中，作为收到的新样本，通过使用投影矩阵将其投影到特征空间中并进行分类。

公开(公告)号：US20090097772A1

公开(公告)日：2009-04-16

申请号：US11871764

申请日：2007-10-12

申请人： Deli Zhao ,  Zhouchen Lin ,  Xiaoou Tang

发明人： Deli Zhao ,  Zhouchen Lin ,  Xiaoou Tang

IPC分类号： G06K9/40

CPC分类号： G06K9/6248

摘要： Systems and methods perform Laplacian Principal Components Analysis (LPCA). In one implementation, an exemplary system receives multidimensional data and reduces dimensionality of the data by locally optimizing a scatter of each local sample of the data. The optimization includes summing weighted distances between low dimensional representations of the data and a mean. The weights of the distances can be determined by a coding length of each local data sample. The system can globally align the locally optimized weighted scatters of the local samples and provide a global projection matrix. The LPCA improves performance of such applications as face recognition and manifold learning.

摘要翻译： 系统和方法执行拉普拉斯主成分分析（LPCA）。 在一个实现中，示例性系统通过局部优化数据的每个局部采样的散射来接收多维数据并且降低数据的维度。 优化包括对数据的低维表示和平均值之间的加权距离求和。 距离的权重可以通过每个本地数据样本的编码长度来确定。 该系统可以对局部采样的局部优化加权散射进行全局对齐，并提供全局投影矩阵。 LPCA可以改善诸如面部识别和歧管学习等应用的性能。