EFFICIENT DISTANCE METRIC LEARNING FOR FINE-GRAINED VISUAL CATEGORIZATION
    1.
    发明公开
    EFFICIENT DISTANCE METRIC LEARNING FOR FINE-GRAINED VISUAL CATEGORIZATION 审中-公开
    EFFIZIENTES LERNEN VON ABSTANDSMETRIKENFÜRFEINKÖRNIGEVISUELLE KATEGORISIERUNG

    公开(公告)号:EP3063656A1

    公开(公告)日:2016-09-07

    申请号:EP14858662.1

    申请日:2014-10-29

    IPC分类号: G06F17/00

    摘要: Methods and systems for distance metric learning include generating two random projection matrices of a dataset from a d-dimensional space into an m-dimensional sub-space, where m is smaller than d. An optimization problem is solved in the m-dimensional subspace to learn a distance metric based on the random projection matrices. The distance metric is recovered in the d-dimensional space.

    摘要翻译: 用于距离度量学习的方法和系统包括从d维空间将数据集的两个随机投影矩阵生成到m小于d的m维子空间中。 在m维子空间中解决优化问题,以基于随机投影矩阵来学习距离度量。 在d维空间中恢复距离度量。