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- W3206103570 abstract "Suppose that we wish to estimate a vector from a set of binary paired comparisons of the form $mathbf{x}$ is closer to $mathbf{p}$ than to $mathbf{q}$ for various choices of vectors $mathbf{p}$ and $mathbf{q}$. The problem of estimating from this type of observation arises in a variety of contexts, including nonmetric multidimensional scaling, unfolding, and ranking problems, often because it provides a powerful and flexible model of preference. We describe theoretical bounds for how well we can expect to estimate under a randomized model for $mathbf{p}$ and $mathbf{q}$. We also present results for the case where the comparisons are noisy and subject to some degree of error. Additionally, we show that under a randomized model for $mathbf{p}$ and $mathbf{q}$, a suitable number of binary paired comparisons yield a stable embedding of the space of target vectors. Finally, we also show that we can achieve significant gains by adaptively changing the distribution for choosing $mathbf{p}$ and $mathbf{q}$." @default.
- W3206103570 created "2021-10-25" @default.
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- W3206103570 date "2021-01-01" @default.
- W3206103570 modified "2023-09-26" @default.
- W3206103570 title "As You Like It: Localization via Paired Comparisons" @default.
- W3206103570 hasPublicationYear "2021" @default.
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