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arXiv · 2003.06076

Joint Alignment From Pairwise Differences with a Noisy Oracle

Michael Mitzenmacher·Charalampos E. Tsourakakis
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Abstract

In this work we consider the problem of recovering $n$ discrete random variables $x_i\in \{0,\ldots,k-1\}, 1 \leq i \leq n$ (where $k$ is constant) with the smallest possible number of queries to a noisy oracle that returns for a given query pair $(x_i,x_j)$ a noisy measurement of their modulo $k$ pairwise difference, i.e., $y_{ij} = (x_i-x_j) \mod k$. This is a joint discrete alignment problem with important applications in computer vision, graph mining, and spectroscopy imaging. Our main result is a polynomial time algorithm that learns exactly with high probability the alignment (up to some unrecoverable offset) using $O(n^{1+o(1)})$ queries.

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Michael Mitzenmacher, Charalampos E. Tsourakakis. 2020-12-06. Joint Alignment From Pairwise Differences with a Noisy Oracle. https://doi.org/10.1007/978-3-319-92871-5_5

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