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Vector data are normally used for probabilistic graphical models with Bayesian inference. However, tensor data, i.e., multidimensional arrays, are actually natural representations of a large amount of real data, in data mining, computer vision, and many other applications. Aiming at breaking the huge gap between vectors and tensors in conventional statistical tasks, e.g., automatic model selection,...
Traditional image representations are not suited to conventional classification methods such as the linear discriminant analysis (LDA) because of the undersample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two-dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis...
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