Sparse and Low-Rank High-Order Tensor Regression via Parallel Proximal Method. (arXiv:1911.12965v2 [cs.LG] UPDATED)

Recently, tensor data (or multidimensional array) have been generated in many
modern applications, such as functional magnetic resonance imaging (fMRI) in
neuroscience and videos in video analysis. Many efforts are made in recent
years to predict the relationship between tensor features and univariate
responses. However, previously proposed methods either lose structural
information within tensor data or have prohibitively expensive time costs,
especially for large-scale data with high-order structures. To address such
problems, we propose the Sparse and Low-rank Tensor Regression …

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