IEEE Trans Image Process - A linear support higher-order tensor machine for classification.

Tópicos

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Resumo

There has been growing interest in developing more effective learning machines for tensor classification. At present, most of the existing learning machines, such as support tensor machine (STM), involve nonconvex optimization problems and need to resort to iterative techniques. Obviously, it is very time-consuming and may suffer from local minima. In order to overcome these two shortcomings, in this paper, we present a novel linear support higher-order tensor machine (SHTM) which integrates the merits of linear C-support vector machine (C-SVM) and tensor rank-one decomposition. Theoretically, SHTM is an extension of the linear C-SVM to tensor patterns. When the input patterns are vectors, SHTM degenerates into the standard C-SVM. A set of experiments is conducted on nine second-order face recognition datasets and three third-order gait recognition datasets to illustrate the performance of the proposed SHTM. The statistic test shows that compared with STM and C-SVM with the RBF kernel, SHTM provides significant performance gain in terms of test accuracy and training speed, especially in the case of higher-order tensors.

Resumo Limpo

grow interest develop effect learn machin tensor classif present exist learn machin support tensor machin stm involv nonconvex optim problem need resort iter techniqu obvious timeconsum may suffer local minima order overcom two shortcom paper present novel linear support higherord tensor machin shtm integr merit linear csupport vector machin csvm tensor rankon decomposit theoret shtm extens linear csvm tensor pattern input pattern vector shtm degener standard csvm set experi conduct nine secondord face recognit dataset three thirdord gait recognit dataset illustr perform propos shtm statist test show compar stm csvm rbf kernel shtm provid signific perform gain term test accuraci train speed especi case higherord tensor

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