Learning Latent Variable Models by Improving Spectral Solutions with Exterior Point Method

@inproceedings{Shaban2015LearningLV,
  title={Learning Latent Variable Models by Improving Spectral Solutions with Exterior Point Method},
  author={Amirreza Shaban and Mehrdad Farajtabar and Bo Xie and Le Song and Byron Boots},
  booktitle={UAI},
  year={2015}
}
Probabilistic latent-variable models are a fundamental tool in statistics and machine learning. Despite their widespread use, identifying the parameters of basic latent variable models continues to be an extremely challenging problem. Traditional maximum likelihood-based learning algorithms find valid parameters, but suffer from high computational cost, slow convergence, and local optima. In contrast, recently developed spectral algorithms are computationally efficient and provide strong… CONTINUE READING
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Exterior-Point Algorithms for Solving LargeScale Nonlinear Optimization Problems

  • V. J. Bloom
  • PhD thesis, George Mason University,
  • 2014
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