Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation

@inproceedings{Zhang2016GlobalCO,
  title={Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation},
  author={Dejiao Zhang and Laura Balzano},
  booktitle={AISTATS},
  year={2016}
}
It has been observed in a variety of contexts that gradient descent methods have great success in solving low-rank matrix factorization problems, despite the relevant problem formulation being non-convex. We tackle a particular instance of this scenario, where we seek the d-dimensional subspace spanned by a streaming data matrix. We apply the natural first order incremental gradient descent method, constraining the gradient method to the Grassmannian. In this paper, we propose an adaptive step… CONTINUE READING
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