Exact Schur-Sylvester reductions lower PPMH projection and volume costs for non-smooth NML from O(N^3) to O(k^3 + N^2 k), with reported 14,100x speedups on high-dimensional data while preserving double-precision equivalence.
A probabilistic basis for low-rank matrix learning,
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Exact Schur-Sylvester Dimensionality Reductions for Non-Smooth Stochastic Complexity and Manifold Sampling
Exact Schur-Sylvester reductions lower PPMH projection and volume costs for non-smooth NML from O(N^3) to O(k^3 + N^2 k), with reported 14,100x speedups on high-dimensional data while preserving double-precision equivalence.