Abstract
Tensors are increasingly encountered in prediction problems. We extend previous results for high-dimensional least-squares convex tensor regression to classification problems with a hinge loss and establish its asymptotic statistical properties. Based on a general convex decomposable penalty, the rate depends on both the intrinsic dimension and the Rademacher complexity of the class of linear functions of tensor predictors.
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