Double-centered rank-d spectral truncation recovers normalized latent inner products from dense anisotropic Gaussian random geometric graphs at a stable-rank rate matching isotropic SOTA.
[Ver18] Roman Vershynin.High-Dimensional Probability: An Introduction with Applications in Data Science, volume 47 ofCambridge Series in Statistical and Probabilistic Math- ematics
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Recovery of latent inner products from an anisotropic Gaussian random geometric graph
Double-centered rank-d spectral truncation recovers normalized latent inner products from dense anisotropic Gaussian random geometric graphs at a stable-rank rate matching isotropic SOTA.