Uniqueness of Ordinal Embedding

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Matthäus Kleindessner, Ulrike Luxburg ;
Proceedings of The 27th Conference on Learning Theory, PMLR 35:40-67, 2014.

Abstract

Ordinal embedding refers to the following problem: all we know about an unknown set of points x_1,\ldots, x_n ∈\mathbbR^d are ordinal constraints of the form \|x_i - x_j\| < \|x_k - x_l\|; the task is to construct a realization y_1,\ldots, y_n ∈\mathbbR^d that preserves these ordinal constraints. It has been conjectured since the 1960ies that upon knowledge of all ordinal constraints a large but finite set of points can be approximately reconstructed up to a similarity transformation. The main result of our paper is a formal proof of this conjecture.

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