Supervised Nonnegative Tensor Factorization with Maximum-Margin Constraint

Non-negative tensor factorization (NTF) has attracted great attention in the machine learning community. In this paper, we extend traditional non-negative tensor factorization into a supervised discriminative decomposition, referred as Supervised Non-negative Tensor Factorization with Maximum-Margin Constraint (SNTFM2 ). SNTFM2 formulates the optimal discriminative factorization of non-negative tensorial data as a coupled least-squares optimization problem via a maximum-margin method. As a result, SNTFM2 not only faithfully approximates the tensorial data by additive combinations of the basis, but also obtains a strong generalization power to discriminative analysis (in particular for classification in this paper). The experimental results show the superiority of our proposed model over state-of-the-art techniques on both toy and real world data sets.

Authors

• Fei Wu Zhejiang University
• Xu Tan Microsoft
• Yi Yang
• Dacheng Tao College of Computing and Data Science, Nanyang Technological University
• Siliang Tang Zhejiang University
• Yueting Zhuang Zhejiang University

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