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Permutations Unlabeled Beyond Sampling Unknown
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DOI：10.1109/LSP.2019.2908505.png)
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Abstract
En 中文
A recent unlabeled sampling result by Unnikrishnan, Haghighatshoar, and Vetterli states that with probability one over Gaussian random matrices A with iid entries, any x can be uniquely recovered from an unknown permutation of y = Ax as soon as A has at least twice as many rows as columns. We show that this condition on Aimplies something much stronger: that an unknown vector x can be recovered from measurements y = T Ax, when the unknown T belongs to an arbitrary set of invertible, diagonalizable linear transformations T. The set T can be finite or countably infinite. When it is the set of m x m permutation matrices, we have the classical unlabeled sampling problem. We show that for almost all A with at least twice as many rows as columns, all x can be recovered either uniquely, or up to a scale depending on T, and that the condition on the size of A is necessary. Our proof is based on vector space geometry. Specializing to permutations, we obtain a simplified proof of the uniqueness result of Unnikrishnan, Haghighatshoar, and Vetterli. In this letter, we are only concerned with uniqueness; stability and algorithms are left for future work.
Keywords:
Sampling
shuffled regression
unlabeled sampling
unknown permutation
unknown transformation
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