Intensity Dot Product Graphs generate random graphs via Poisson point processes on latent space with dot-product affinities, defining heat maps and desire operators while proving spectral consistency to the operator spectrum.
Matrix estimation by universal singular value thresholding.The Annals of Statistics, 43(1):177–214
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Nonconvex low-rank matrix estimation procedures are shown to be equivalent to locally strongly convex formulations via a benign regularizer that does not change the algorithm's update rule.
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Intensity Dot Product Graphs
Intensity Dot Product Graphs generate random graphs via Poisson point processes on latent space with dot-product affinities, defining heat maps and desire operators while proving spectral consistency to the operator spectrum.
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Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation
Nonconvex low-rank matrix estimation procedures are shown to be equivalent to locally strongly convex formulations via a benign regularizer that does not change the algorithm's update rule.