A second-order method achieves local quadratic convergence on the Stiefel manifold without retractions by combining a modified Newton tangent step with Newton-Schulz normal steps for constraint satisfaction.
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FastUMAP approximates UMAP via sparse bipartite point-landmark graphs and Nystrom initialization to deliver lower runtimes than Barnes-Hut t-SNE on most tested datasets while retaining competitive kNN accuracy.
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A second-order method landing on the Stiefel manifold via Newton$\unicode{x2013}$Schulz iteration
A second-order method achieves local quadratic convergence on the Stiefel manifold without retractions by combining a modified Newton tangent step with Newton-Schulz normal steps for constraint satisfaction.