We frame max-margin learning of latent variable structured prediction models as a convex optimization problem, making use of scoring functions computed by input-output observable operator models. This learning problem can be expressed as an optimization problem involving a low-rank Hankel matrix that represents the inputoutput operator model. The direct outcome of our work is a new spectral regularization method for max-margin structured prediction. Our experiments confirm that our proposed regularization framework leads to an effective way of controlling the capacity of structured prediction models.
CitationQuattoni, A. [et al.]. Spectral regularization for max-margin sequence tagging. A: International Conference on Machine Learning. "Proceedings of the 31st International Conference on Machine Learning.". Beijing: 2014, p. 1-9.
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