A New Convex Relaxation for Tensor Completion

Part of Advances in Neural Information Processing Systems 26 (NIPS 2013)

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Bernardino Romera-Paredes, Massimiliano Pontil


We study the problem of learning a tensor from a set of linear measurements. A prominent methodology for this problem is based on the extension of trace norm regularization, which has been used extensively for learning low rank matrices, to the tensor setting. In this paper, we highlight some limitations of this approach and propose an alternative convex relaxation on the Euclidean unit ball. We then describe a technique to solve the associated regularization problem, which builds upon the alternating direction method of multipliers. Experiments on one synthetic dataset and two real datasets indicate that the proposed method improves significantly over tensor trace norm regularization in terms of estimation error, while remaining computationally tractable.