基于张量核范数的支持张量机

Support Tensor Machine Based on Nuclear Norm of Tensor

通过引入张量的核范数,结合张量的展开矩阵等性质,提出了一种基于张量核范数的支持张量机(STM-NNT)并且构建了相应的算法来更有效地解决张量的分类问题.该方法通过引入一个核范数正则项来控制权重矩阵的秩,避免了过学习现象,达到了稀疏学习的目的.

A novel method,called support tensor machine based on nuclear norm of the tensor（STM-NNT）,is proposed by the introduction of tensor nuclear norm,and corresponding algorithm is constructed for the efficient settlement of the classifying problem of tensor.The said method,by introducing a nuclear norm regular term thus to control the rank of weight matrix,saves us from over learning and thus to achieve the goal of sparse learning.