Abstract:We study the problem of supervised learning a metric space under discriminative constraints. Given a universe X and sets S, D Ă `X 2 ˘of similar and dissimilar pairs, we seek to find a mapping f : X Ñ Y , into some target metric space M " pY, ρq, such that similar objects are mapped to points at distance at most u, and dissimilar objects are mapped to points at distance at least ℓ. More generally, the goal is to find a mapping of maximum accuracy (that is, fraction of correctly classified pairs). We propose ap… Show more
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