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Abstract:
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Tensor is a multiway array. With the rapid development of science and technology in the past decades, large amount of tensor observations are routinely collected, processed, and stored in many scientific researches and commercial activities nowadays. Driven by the need to address data analysis challenges that arise in tensor data, we propose a tensor dimension reduction model, a model assuming the nonlinear dependence between a response and a projection of all the tensor predictors. The tensor dimension reduction models are estimated in a sequential iterative fashion. Empirical performance demonstrates that our proposed method can greatly improve the sensitivity and specificity of the real data.
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