This paper focuses on developing kernelized tensor factor- ization with kernel maximum-margin constraint, referred as Kernelized Support Tensor Machine (KSTM).
Based on tensor factorization theory and kernel methods, we propose a novel Kernelized Support Tensor Machine (KSTM) which integrates kernelized tensor ...
This paper has proposed a tensor train (TT)-based kernel trick and devised a kernelized support tensor train machine (K-STTM). Extensive experiments have ...
This paper focuses on developing kernelized tensor factor- ization with kernel maximum-margin constraint, referred as Kernelized Support Tensor Machine (KSTM).
Jan 2, 2020 · In this paper, we propose a tensor train (TT)-based kernel technique for the first time, and apply it to the conventional support vector machine ...
In this paper, we propose a tensor train (TT)-based kernel technique for the first time, and apply it to the conventional support vector machine (SVM) for high- ...
Based on tensor factorization theory and kernel methods, we propose a novel Kernelized Support Tensor Machine (KSTM) which integrates kernelized tensor ...
In this paper, we propose a tensor train (TT)-based kernel technique for the first time, and apply it to the conventional support vector machine (SVM) for high- ...
Jan 2, 2020 · In this paper, we propose a tensor train (TT)-based kernel technique for the first time, and apply it to the conventional support vector machine ...
A Kernelized Support Tensor-Ring Machine for High-Dimensional Data ...
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Specifically, tensor-ring (TR) and kernel methods are utilized to the support vector machine (SVM). And we construct a TR decomposition based kernel function.