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Some theory for ordinal embedding

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arxiv 1501.02861 v2 pith:CQLCV23R submitted 2015-01-13 math.ST stat.TH

Ery Arias-Castro This is my paper · ORCID
classification math.STstat.TH
keywords comparisonsembeddingconsistencyordinalproblemachieveboundconsider
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Motivated by recent work on ordinal embedding (Kleindessner and von Luxburg, 2014), we derive large sample consistency results and rates of convergence for the problem of embedding points based on triple or quadruple distance comparisons. We also consider a variant of this problem where only local comparisons are provided. Finally, inspired by (Jamieson and Nowak, 2011), we bound the number of such comparisons needed to achieve consistency.

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  1. Estimation of perceptual scales using ordinal embedding

    cs.LG 2019-08 conditional novelty 6.0 of 10

    Ordinal embedding methods recover one-dimensional monotonic perceptual scales as accurately as MLDS and can additionally recover non-monotonic and multi-dimensional scales from triplet comparisons.

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