High-Dimensional Procrustes Matching via Tree Counts

• URL: http://arxiv.org/abs/2607.08538v1
• Date: Thu, 09 Jul 2026 14:33:47 GMT
• Title: High-Dimensional Procrustes Matching via Tree Counts
• Abstract summary: Procrustes matching problem asks us to recover the unknown permutation of $[n]$ that aligns the two sets.<n>We show that exact recovery is possible when $2 gtrsim maxlog n/d,sqrtlog n/n$.<n>We also carry out a low-degree advantage calculation, which suggests that condition $2 > sqrt$ is necessary for any tree-counting algorithm.
• Score: 5.464035038606368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Suppose we observe two sets of $n$ Gaussian vectors in $\mathbb{R}^d$, with the promise that, after applying a permutation of $[n]$ and a rotation of $\mathbb{R}^d$, the two sets are $ρ$-correlated. The Procrustes matching problem asks us to recover the unknown permutation of $[n]$ that aligns the two sets. The problem is well-studied in the low-dimensional regime $d=O(\log n)$, but the high-dimensional regime $d\gg \log n$ has remained largely uncharted: prior matching guarantees require nearly perfect correlation $ρ=1-o(1)$, even for information-theoretic recovery. Our main result is a polynomial-time algorithm for exact recovery at constant correlation. The algorithm works by computing and comparing weighted counts of a specially chosen family of ``wide'' trees. So long as $d\ge \mathrm{polylog}(n)$, the algorithm succeeds with high probability for any $ρ^2>\sqrtα$, where $α\approx 0.338$ is Otter's tree-counting constant. We complement this algorithmic result with an improved information-theoretic guarantee, showing that exact recovery is possible when $ρ^2 \gtrsim \max\{\log n/d,\sqrt{\log n/n}\}$. We also carry out a low-degree advantage calculation, which suggests that the condition $ρ^2 > \sqrtα$ is necessary for any tree-counting algorithm.

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