Abstract: Most data used by traditional machine learning methods belong to vector space.As an important machine learning method,support vector machine (SVM) has better performance in solving small samples,nonlinearity and high-dimensionality problems.However,in practical applications,some data like images and videos are both stored in tensor form.If tensor data are convert into vector data,some original structure and related information may be lost,which will cause dimensional disasters and small samples problems.Therefore,to maintain as much tensor structure information as possible,support tensor machine (STM) based on Tucker decomposition was proposed.Experiments show that this method can significantly improve the classification performance.Meanwhile,as a supervised learning method,support vector machine cannot use unlabeled data,which often encounter problems with insufficient training data.Therefore,semi-supervised support tensor machine based on Tucker decomposition was proposed.This algorithm can not only maintain more tensor structure information,but also make full use of the unlabeled data.Experiments show that the prediction accuracy rate is 90.26%,which validates the effectiveness of the proposed method.

Key words: Semi-supervised learning, Support tensor machine, Tucker decomposition