Scalable semidefinite manifold learning

@article{Vasiloglou2008ScalableSM,
  title={Scalable semidefinite manifold learning},
  author={Nikolaos Vasiloglou and A. G. Gray and D. V. Anderson},
  journal={2008 IEEE Workshop on Machine Learning for Signal Processing},
  year={2008},
  pages={368-373}
}
Maximum variance unfolding (MVU) is among the state of the art manifold learning (ML) algorithms and experimentally proven to be the best method to unfold a manifold to its intrinsic dimension. Unfortunately it doesnpsilat scale for more than a few hundred points. A non convex formulation of MVU made it possible to scale up to a few thousand points with the risk of getting trapped in local minima. In this paper we demonstrate techniques based on the dual-tree algorithm and L-BFGS that allow MVU… CONTINUE READING
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