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    FAST TRAINING OF SUPPORT VECTOR DATA DESCRIPTION USING SAMPLING
    1.
    发明申请
    FAST TRAINING OF SUPPORT VECTOR DATA DESCRIPTION USING SAMPLING 有权

    公开(公告)号：US20170323221A1

    公开(公告)日：2017-11-09

    申请号：US15185277

    申请日：2016-06-17

    Applicant: SAS Institute Inc.

    Inventor： Arin Chaudhuri ,  Deovrat Vijay Kakde ,  Maria Jahja ,  Wei Xiao ,  Seung Hyun Kong ,  Hansi Jiang ,  Sergiy Peredriy

    IPC: G06N99/00 ,  H04L29/08 ,  G06F17/30

    CPC classification number: G06N99/005 ,  G06F17/30539 ,  H04L67/02

    Abstract: A computing device determines an SVDD to identify an outlier in a dataset. First and second sets of observation vectors of a predefined sample size are randomly selected from a training dataset. First and second optimal values are computed using the first and second observation vectors to define a first set of support vectors and a second set of support vectors. A third optimal value is computed using the first set of support vectors updated to include the second set of support vectors to define a third set of support vectors. Whether or not a stop condition is satisfied is determined by comparing a computed value to a stop criterion. When the stop condition is not satisfied, the first set of support vectors is defined as the third set of support vectors, and operations are repeated until the stop condition is satisfied. The third set of support vectors is output.

    Incremental singular value decomposition in support of machine learning
    3.
    发明授权
    Incremental singular value decomposition in support of machine learning 有权

    公开(公告)号：US11314844B1

    公开(公告)日：2022-04-26

    申请号：US17504634

    申请日：2021-10-19

    Applicant: SAS Institute Inc.

    Inventor： Hansi Jiang ,  Arin Chaudhuri

    IPC: G06F17/16

    Abstract: A singular value decomposition (SVD) is computed of a first matrix to define a left matrix, a diagonal matrix, and a right matrix. The left matrix, the diagonal matrix, and the right matrix are updated using an arrowhead matrix structure defined from the diagonal matrix and by adding a next observation vector to a last row of the first matrix. The updated left matrix, the updated diagonal matrix, and the updated right matrix are updated using a diagonal-plus-rank-one (DPR1) matrix structure defined from the updated diagonal matrix and by removing an observation vector from a first row of the first matrix. Eigenpairs of the DPR1 matrix are computed based on whether a value computed from the updated left matrix is positive or negative. The left matrix updated in (C), the diagonal matrix updated in (C), and the right matrix updated in (C) are output.

    Kernel parameter selection in support vector data description for outlier identification
    5.
    发明授权
    Kernel parameter selection in support vector data description for outlier identification 有权
    内核参数选择支持向量数据描述异常值识别

    公开(公告)号：US09536208B1

    公开(公告)日：2017-01-03

    申请号：US15096552

    申请日：2016-04-12

    Applicant: SAS Institute Inc.

    Inventor： Deovrat Vijay Kakde ,  Arin Chaudhuri ,  Seung Hyun Kong ,  Maria Jahja ,  Hansi Jiang ,  Jorge Manuel Gomes da Silva

    IPC: G06N99/00

    CPC classification number: G06N99/005

    Abstract: A computer-readable medium is configured to determine a support vector data description (SVDD). For each of a plurality of values for a kernel parameter, an optimal value of an objective function defined for an SVDD model using a kernel function, a read plurality of data points, and a respective value for the kernel parameter is computed to define a plurality of sets of support vectors. A plurality of first derivative values are computed for the objective function as a difference between the computed optimal values associated with successive values for the kernel parameter. A plurality of second derivative values are computed for the objective function as a difference between the computed plurality of first derivative values associated with successive values for the kernel parameter. A kernel parameter value is selected where the computed plurality of second derivative values first exceeds zero.

    Abstract translation: 计算机可读介质被配置为确定支持向量数据描述（SVDD）。 对于内核参数的多个值中的每一个，计算使用内核函数为SVDD模型定义的目标函数的最佳值，读取的多个数据点和用于内核参数的相应值，以定义多个 的支持向量集。 针对目标函数计算多个一阶导数值，作为与内核参数的连续值相关联的计算的最优值之间的差。 针对目标函数计算多个二阶导数值作为与内核参数的连续值相关联的所计算的多个一阶导数值之间的差。 选择核心参数值，其中所计算的多个二阶导数值首先超过零。

    ANALYTIC SYSTEM TO INCREMENTALLY UPDATE A SUPPORT VECTOR DATA DESCRIPTION FOR OUTLIER IDENTIFICATION
    6.
    发明申请
    ANALYTIC SYSTEM TO INCREMENTALLY UPDATE A SUPPORT VECTOR DATA DESCRIPTION FOR OUTLIER IDENTIFICATION 审中-公开

    公开(公告)号：US20190095400A1

    公开(公告)日：2019-03-28

    申请号：US16030142

    申请日：2018-07-09

    Applicant: SAS Institute Inc.

    Inventor： Hansi Jiang ,  Wenhao Hu ,  Haoyu Wang ,  Deovrat Vijay Kakde ,  Arin Chaudhuri

    IPC: G06F17/16 ,  G06N99/00

    Abstract: A Gaussian similarity matrix is computed between observation vectors. An inverse Gaussian similarity matrix is computed from the Gaussian similarity matrix. A row sum vector is computed that includes a row sum value computed from each row of the inverse Gaussian similarity matrix. (a) A new observation vector is selected. (b) An acceptance value is computed for the new observation vector using the set of boundary support vectors, the row sum vector, and the new observation vector. (c) (a) and (b) are repeated when the computed acceptance value is less than or equal to zero. (d) An incremental vector is computed from the inverse Gaussian similarity matrix and the new observation vector when the computed acceptance value is greater than zero. (e) the selected new observation vector is output as an outlier observation vector when a maximum value of the incremental vector is less than a first predefined tolerance value.

    Fast training of support vector data description using sampling
    7.
    发明授权
    Fast training of support vector data description using sampling 有权

    公开(公告)号：US09830558B1

    公开(公告)日：2017-11-28

    申请号：US15185277

    申请日：2016-06-17

    Applicant: SAS Institute Inc.

    Inventor： Arin Chaudhuri ,  Deovrat Vijay Kakde ,  Maria Jahja ,  Wei Xiao ,  Seung Hyun Kong ,  Hansi Jiang ,  Sergiy Peredriy

    IPC: G06F15/18 ,  G06N99/00 ,  H04L29/08 ,  G06F17/30

    CPC classification number: G06N99/005 ,  G06F17/30539 ,  H04L67/02

    Abstract: A computing device determines an SVDD to identify an outlier in a dataset. First and second sets of observation vectors of a predefined sample size are randomly selected from a training dataset. First and second optimal values are computed using the first and second observation vectors to define a first set of support vectors and a second set of support vectors. A third optimal value is computed using the first set of support vectors updated to include the second set of support vectors to define a third set of support vectors. Whether or not a stop condition is satisfied is determined by comparing a computed value to a stop criterion. When the stop condition is not satisfied, the first set of support vectors is defined as the third set of support vectors, and operations are repeated until the stop condition is satisfied. The third set of support vectors is output.

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