公开(公告)号：US09542654B2

公开(公告)日：2017-01-10

申请号：US14339994

申请日：2014-07-24

申请人： Xerox Corporation

发明人： Guillaume Bouchard ,  Cedric Philippe Charles Jean Ghislain Archambeau ,  Behrouz Behmardi

IPC分类号： G06N99/00 ,  G06F17/30

CPC分类号： G06N99/005 ,  G06F17/30 ,  G06F17/30601 ,  G06N7/00

摘要： In multi-view learning, optimized prediction matrices are determined for V≧2 views of n objects, and a prediction of a view of an object is generated based on the optimized prediction matrix for that view. An objective is optimized, wherein is a set of parameters including at least the V prediction matrices and a concatenated matrix comprising a concatenation of the prediction matrices, and comprises a sum including at least a loss function for each view, a trace norm of the prediction matrix for each view, and a trace norm of the concatenated matrix. may further include a sparse matrix for each view, with further including an element-wise norm of the sparse matrix for each view. may further include regularization parameters scaling the trace norms of the prediction matrices and the trace norm of the concatenated matrix.

摘要翻译： 在多视图学习中，针对n个对象的V≥2视图确定优化预测矩阵，并且基于该视图的优化预测矩阵来生成对象视图的预测。 优化目标，其中是包括至少V个预测矩阵和包括预测矩阵的级联的级联矩阵的一组参数，并且包括至少包括每个视图的损失函数，预测的跟踪范数 每个视图的矩阵，以及连接矩阵的跟踪范数。 还可以包括用于每个视图的稀疏矩阵，还包括用于每个视图的稀疏矩阵的元素范数。 还可以包括缩放预测矩阵的轨迹范数的正则化参数和级联矩阵的轨迹范数。

公开(公告)号：US20160026925A1

公开(公告)日：2016-01-28

申请号：US14339994

申请日：2014-07-24

申请人： Xerox Corporation

发明人： Guillaume Bouchard ,  Cedric Philippe Charles Jean Ghislain Archambeau ,  Behrouz Behmardi

IPC分类号： G06N7/00 ,  G06N99/00

CPC分类号： G06N99/005 ,  G06F17/30 ,  G06F17/30601 ,  G06N7/00

摘要： In multi-view learning, optimized prediction matrices are determined for V≧2 views of n objects, and a prediction of a view of an object is generated based on the optimized prediction matrix for that view. An objective is optimized, wherein is a set of parameters including at least the V prediction matrices and a concatenated matrix comprising a concatenation of the prediction matrices, and comprises a sum including at least a loss function for each view, a trace norm of the prediction matrix for each view, and a trace norm of the concatenated matrix. may further include a sparse matrix for each view, with further including an element-wise norm of the sparse matrix for each view. may further include regularization parameters scaling the trace norms of the prediction matrices and the trace norm of the concatenated matrix.

摘要翻译： 在多视图学习中，针对n个对象的V≥2视图确定优化预测矩阵，并且基于该视图的优化预测矩阵来生成对象视图的预测。 优化目标，其中是包括至少V个预测矩阵和包括预测矩阵的级联的级联矩阵的一组参数，并且包括至少包括每个视图的损失函数，预测的跟踪范数 每个视图的矩阵，以及连接矩阵的跟踪范数。 还可以包括用于每个视图的稀疏矩阵，还包括用于每个视图的稀疏矩阵的元素范数。 还可以包括缩放预测矩阵的轨迹范数的正则化参数和级联矩阵的轨迹范数。