Reviews: Low-Rank Regression with Tensor Responses

Strength: --The paper provides the theoretical analysis of approximation guarantees and a generalization bound for the class of tensor-valued regression functions. Weakness: --A major drawback is that the novelty and contribution is rather limited. The key idea and the model of this paper is actually equivalent to the HOPLS in the following paper: [Zhao et. In HOPLS, it assumes the tensor input has low-rank structure and also the tensor output has low-rank structure, and the link of them is established in the common latent space. And then follows a regression step against the projected latent variables.