Kernelized support tensor train machines
作者：
Highlights：
• The tensorial data structure is useful and it is better to keep the data structure instead of vectorizing the data.
• Support vector machine is extended to a kernelized support tensor train machine, which accepts tensorial input directly.
• Tensor train based kernel mapping scheme is proposed and the validity proof of the kernel mapping is also proved.
• Proposing a data decomposition scheme to make sure that similar tensors have similar kernel mappings in the feature space.
• Doable to apply different kernel functions on different tensorial data modes.
摘要
•The tensorial data structure is useful and it is better to keep the data structure instead of vectorizing the data.•Support vector machine is extended to a kernelized support tensor train machine, which accepts tensorial input directly.•Tensor train based kernel mapping scheme is proposed and the validity proof of the kernel mapping is also proved.•Proposing a data decomposition scheme to make sure that similar tensors have similar kernel mappings in the feature space.•Doable to apply different kernel functions on different tensorial data modes.
论文关键词：Image classification,Tensor,Support tensor machine
论文评审过程：Received 13 September 2020, Revised 15 September 2021, Accepted 18 September 2021, Available online 20 September 2021, Version of Record 1 October 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108337
