Exact threshold for approximate ellipsoid fitting of random points
Afonso S. Bandeira, Antoine Maillard

TL;DR
This paper rigorously determines the sharp phase transition at in the problem of fitting ellipsoids to high-dimensional Gaussian points, revealing when solutions exist or not as dimensions grow.
Contribution
It establishes the exact threshold for the existence of ellipsoid fits in high dimensions, using a universality approach and rigorous analysis.
Findings
For , ellipsoid fitting is possible with bounded axes.
For , small fitting error is impossible unless the shortest axis shrinks to zero.
The phase transition at is characterized rigorously for the first time.
Abstract
We consider the problem of exactly fitting an ellipsoid (centered at ) to standard Gaussian random vectors in , as with . This problem is conjectured to undergo a sharp transition: with high probability, has a solution if , while has no solutions if . So far, only a trivial bound is known to imply the absence of solutions, while the sharpest results on the positive side assume (for a small constant) to prove that is solvable. In this work we show a universality property for the minimal fitting error achievable by ellipsoids: we show that, to leading order, it coincides with the minimal error in a so-called "Gaussian equivalent" problem, for which the satisfiability transition can be rigorously analyzed. Our main…
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Taxonomy
TopicsAdvanced Statistical Methods and Models · Statistical Methods and Inference · Bayesian Methods and Mixture Models
