Optimization over covariance matrices with a parameterized metric
The choice of Riemannian metric can strongly influence the convergence of gradient-based optimization over covariance matrices. Euclidean, Bures-Wasserstein and affine-invariant metrics are common choices, but their relative effectiveness depends on the objective. We introduce a two-parameter family defined by $X^{p}LX...
Yi-Bang Li, Bamdev Mishra, P. Jawanpuria et al.
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