On Tensor Completion via Nuclear Norm Minimization
This work addresses tensor recovery challenges for applications in data analysis and machine learning, offering a more efficient method with theoretical guarantees.
The paper tackles the problem of tensor completion by showing that existing methods that unfold tensors into matrices are suboptimal, and it proposes a convex optimization approach that directly minimizes the tensor nuclear norm, proving an improved sample size requirement.
Many problems can be formulated as recovering a low-rank tensor. Although an increasingly common task, tensor recovery remains a challenging problem because of the delicacy associated with the decomposition of higher order tensors. To overcome these difficulties, existing approaches often proceed by unfolding tensors into matrices and then apply techniques for matrix completion. We show here that such matricization fails to exploit the tensor structure and may lead to suboptimal procedure. More specifically, we investigate a convex optimization approach to tensor completion by directly minimizing a tensor nuclear norm and prove that this leads to an improved sample size requirement. To establish our results, we develop a series of algebraic and probabilistic techniques such as characterization of subdifferetial for tensor nuclear norm and concentration inequalities for tensor martingales, which may be of independent interests and could be useful in other tensor related problems.