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    • 76. 发明申请
    • SYSTEMS, METHODS AND COMPUTER PROGRAM PRODUCTS FOR SUPERVISED DIMENSIONALITY REDUCTION WITH MIXED-TYPE FEATURES AND LABELS
    • 用于具有混合类型特征和标签的超大尺寸减小的系统,方法和计算机程序产品
    • US20090210363A1
    • 2009-08-20
    • US12031775
    • 2008-02-15
    • Genady GrabarnikIrina Rish
    • Genady GrabarnikIrina Rish
    • G06F15/18
    • G06K9/6247
    • Systems, methods and computer program products for supervised dimensionality reduction. Exemplary embodiments include a method including receiving an input in the form of a data matrix X of size N×D, wherein N is a number of samples, D is a dimensionality, a vector Y of size N×1, hidden variables U of a number K, a data type of the matrix X and the vector Y, and a trade-off constant alpha; selecting loss functions in the form of Lx(X,UV) and Ly(Y,UW) appropriate for the type of data in the matrix X and the vector Y, where U, V and W are matrices, selecting corresponding sets of update rules RU, RV and RW for updating the matrices U,V and W, learning U, V and W that provide a minimum total loss L(U,V,W)=Lx(X,UV)+alpha*Ly(Y,UW), and returning matrices U, V and W.
    • 用于监督维度降低的系统,方法和计算机程序产品。 示例性实施例包括一种方法,包括以尺寸N×D的数据矩阵X的形式接收输入,其中N是采样数,D是维度,尺寸N×1的向量Y,数字K的隐藏变量U, 矩阵X的数据类型和向量Y,以及权衡常数α; 选择适合矩阵X中的数据类型的Lx(X,UV)和Ly(Y,UW)的形式的损失函数,其中U,V和W是矩阵,选择相应的更新规则集 RU,RV和RW用于更新矩阵U,V和W,学习U,V和W,其提供最小总损耗L(U,V,W)= Lx(X,UV)+α* Ly(Y,UW )和返回矩阵U,V和W.