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.
[BBN20] Matthew Brennan, Guy Bresler, and Dheeraj Nagaraj
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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.