Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials

@inproceedings{Wang2014EfficientIO,
  title={Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials},
  author={Shenlong Wang and Alexander G. Schwing and Raquel Urtasun},
  booktitle={NIPS},
  year={2014}
}
In this paper, we prove that every multivariate polynomial with even degree can be decomposed into a sum of convex and concave polynomials. Motivated by this property, we exploit the concave-convex procedure to perform inference on continuous Markov random fields with polynomial potentials. In particular, we show that the concave-convex decomposition of polynomials can be expressed as a sum-of-squares optimization, which can be efficiently solved via semidefinite programing. We demonstrate the… CONTINUE READING
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