2015
DOI: 10.1002/cpa.21588
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Data‐Driven Optimal Transport

Abstract: The problem of optimal transport between two distributions ρ(x) and μ(y) is extended to situations where the distributions are only known through a finite number of samples {xi} and {yj}. A weak formulation is proposed, based on the dual of the Kantorovich formulation, with two main modifications: replacing the expected values in the objective function by their empirical means over the {xi} and {yj}, and restricting the dual variables u(x) and v(y) to a suitable set of test functions adapted to the local avail… Show more

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Cited by 26 publications
(25 citation statements)
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“…In addition to the ISHLT, we used a 3-class system we developed following an unsupervised analysis described elsewhere. 23 - 25 Briefly, the output that the procedure provides is the matrix P0 (class, sample) containing the probability that each sample belongs to each class, and the subset of genes whose expression is most consistent with the classification found. To assign samples to classes, we evolve from a noninformative small random perturbation of the uniform assignment to its final value through a Bayesian procedure that uses the expression of each gene as new evidence to relax P0, thought of as a prior, toward the corresponding posterior.…”
Section: Methodsmentioning
confidence: 99%
“…In addition to the ISHLT, we used a 3-class system we developed following an unsupervised analysis described elsewhere. 23 - 25 Briefly, the output that the procedure provides is the matrix P0 (class, sample) containing the probability that each sample belongs to each class, and the subset of genes whose expression is most consistent with the classification found. To assign samples to classes, we evolve from a noninformative small random perturbation of the uniform assignment to its final value through a Bayesian procedure that uses the expression of each gene as new evidence to relax P0, thought of as a prior, toward the corresponding posterior.…”
Section: Methodsmentioning
confidence: 99%
“…The dual formulation in (3.8) involves the distributions k .x/ only through integrals representing the expected values of the k .x/ under k (a fact exploited in [24] to implement regular data-driven optimal transport). Thus, if only samples from k are available, it is natural to replace these expected values by empirical means:…”
Section: Data-driven Formulationmentioning
confidence: 99%
“…For instance, one could extend the flow-based procedure developed for the data-driven optimal transport problem [24]. For the remainder of this article, we will instead adopt two much simpler, nonadaptive poorman procedures.…”
Section: Poor-man Solutionsmentioning
confidence: 99%
See 1 more Smart Citation
“…Data-driven formulations take as input not the distributions 1;2 but sample sets from both. Methodologies proposed include a fluid-flow-like algorithm [44] and an adaptive linear programming approach [9].…”
Section: Introductionmentioning
confidence: 99%