Riemannian stochastic variance reduced gradient on Grassmann manifold

  title={Riemannian stochastic variance reduced gradient on Grassmann manifold},
  author={Hiroyuki Kasai and Hiroyuki Sato and Bamdev Mishra},
Stochastic variance reduction algorithms have recently become popular for minimizing the average of a large, but finite, number of loss functions. In this paper, we propose a novel Riemannian extension of the Euclidean stochastic variance reduced gradient algorithm (R-SVRG) to a compact manifold search space. To this end, we show the developments on the Grassmann manifold. The key challenges of averaging, addition, and subtraction of multiple gradients are addressed with notions like logarithm… CONTINUE READING
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