Accelerating Block Coordinate Descent for Nonnegative Tensor Factorization

@article{Ang2020AcceleratingBC,
  title={Accelerating Block Coordinate Descent for Nonnegative Tensor Factorization},
  author={A. Ang and J{\'e}r{\'e}my E. Cohen and Nicolas Gillis and L. Hien},
  journal={ArXiv},
  year={2020},
  volume={abs/2001.04321}
}
This paper is concerned with improving the empirical convergence speed of block-coordinate descent algorithms for approximate nonnegative tensor factorization (NTF). We propose an extrapolation strategy in-between block updates, referred to as heuristic extrapolation with restarts (HER). HER significantly accelerates the empirical convergence speed of most existing block-coordinate algorithms for dense NTF, in particular for challenging computational scenarios, while requiring a negligible… Expand
Computing Large-Scale Matrix and Tensor Decomposition With Structured Factors: A Unified Nonconvex Optimization Perspective

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