1 research outputs found
Joint Alignment From Pairwise Differences with a Noisy Oracle
In this work we consider the problem of recovering discrete random
variables (where is constant)
with the smallest possible number of queries to a noisy oracle that returns for
a given query pair a noisy measurement of their modulo pairwise
difference, i.e., . 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 queries.Comment: Paper appeared in the 15th Workshop on Algorithms and Models for the
Web Graph (WAW 2018), invited to Internet Mathematics special issue. Overlaps
in text with earlier unpublished note arxiv:1609.00750. (v2 minor updates