Schuhmacher, Dominic

[["dc.bibliographiccitation.firstpage","953"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Statistics and Computing"],["dc.bibliographiccitation.lastpage","972"],["dc.bibliographiccitation.volume","30"],["dc.contributor.author","Müller, Raoul"],["dc.contributor.author","Schuhmacher, Dominic"],["dc.contributor.author","Mateu, Jorge"],["dc.date.accessioned","2020-12-10T14:11:54Z"],["dc.date.available","2020-12-10T14:11:54Z"],["dc.date.issued","2020"],["dc.description.abstract","We introduce the transport–transform and the relative transport–transform metrics between finite point patterns on a general space, which provide a unified framework for earlier point pattern metrics, in particular the generalized spike time and the normalized and unnormalized optimal subpattern assignment metrics. Our main focus is on barycenters, i.e., minimizers of a q-th-order Fréchet functional with respect to these metrics. We present a heuristic algorithm that terminates in a local minimum and is shown to be fast and reliable in a simulation study. The algorithm serves as a general plug-in method that can be applied to point patterns on any state space where an appropriate algorithm for solving the location problem for individual points is available. We present applications to geocoded data of crimes in Euclidean space and on a street network, illustrating that barycenters serve as informative summary statistics. Our work is a first step toward statistical inference in covariate-based models of repeated point pattern observations."],["dc.identifier.doi","10.1007/s11222-020-09932-y"],["dc.identifier.eissn","1573-1375"],["dc.identifier.issn","0960-3174"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/71248"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","DOI-Import GROB-394"],["dc.relation","RTG 2088: Research Training Group 2088 Discovering structure in complex data: Statistics meets Optimization and Inverse Problems"],["dc.rights","CC BY 4.0"],["dc.title","Metrics and barycenters for point pattern data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","271"],["dc.bibliographiccitation.journal","IEEE Access"],["dc.bibliographiccitation.lastpage","282"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Schrieber, Joern"],["dc.contributor.author","Schuhmacher, Dominic"],["dc.contributor.author","Gottschlich, Carsten"],["dc.date.accessioned","2018-11-07T10:29:01Z"],["dc.date.available","2018-11-07T10:29:01Z"],["dc.date.issued","2017"],["dc.description.abstract","The Wasserstein metric or earth mover's distance is a useful tool in statistics, computer science and engineering with many applications to biological or medical imaging, among others. Especially in the light of increasingly complex data, the computation of these distances via optimal transport is often the limiting factor. Inspired by this challenge, a variety of new approaches to optimal transport has been proposed in recent years and along with these new methods comes the need for a meaningful comparison. In this paper, we introduce a benchmark for discrete optimal transport, called DOTmark, which is designed to serve as a neutral collection of problems, where discrete optimal transport methods can be tested, compared with one another, and brought to their limits on large-scale instances. It consists of a variety of grayscale images, in various resolutions and classes, such as several types of randomly generated images, classical test images and real data from microscopy. Along with the DOTmark we present a survey and a performance test for a cross section of established methods ranging from more traditional algorithms, such as the transportation simplex, to recently developed approaches, such as the shielding neighborhood method, and including also a comparison with commercial solvers."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2016"],["dc.identifier.doi","10.1109/ACCESS.2016.2639065"],["dc.identifier.gro","3145880"],["dc.identifier.isi","000396132600013"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14253"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/43551"],["dc.notes.intern","mathe"],["dc.notes.intern","DOI-Import GROB-394"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Institute of Electrical and Electronics Engineers (IEEE)"],["dc.relation","RTG 2088: Research Training Group 2088 Discovering structure in complex data: Statistics meets Optimization and Inverse Problems"],["dc.relation.eissn","2169-3536"],["dc.relation.issn","2169-3536"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0/"],["dc.subject","earth mover’s distance Optimal transport benchmark Wasserstein metric Benchmark testing Image resolution Microscopy Extraterrestrial measurements Shape Transportation"],["dc.title","DOTmark - A Benchmark for Discrete Optimal Transport"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e110214"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Schuhmacher, Dominic"],["dc.date.accessioned","2017-09-07T11:50:30Z"],["dc.date.available","2017-09-07T11:50:30Z"],["dc.date.issued","2014"],["dc.description.abstract","Finding solutions to the classical transportation problem is of great importance, since this optimization problem arises in many engineering and computer science applications. Especially the Earth Mover's Distance is used in a plethora of applications ranging from content-based image retrieval, shape matching, fingerprint recognition, object tracking and phishing web page detection to computing color differences in linguistics and biology. Our starting point is the well-known revised simplex algorithm, which iteratively improves a feasible solution to optimality. The Shortlist Method that we propose substantially reduces the number of candidates inspected for improving the solution, while at the same time balancing the number of pivots required. Tests on simulated benchmarks demonstrate a considerable reduction in computation time for the new method as compared to the usual revised simplex algorithm implemented with state-of-the-art initialization and pivot strategies. As a consequence, the Shortlist Method facilitates the computation of large scale transportation problems in viable time. In addition we describe a novel method for finding an initial feasible solution which we coin Modified Russell's Method."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2014"],["dc.identifier.doi","10.1371/journal.pone.0110214"],["dc.identifier.gro","3145883"],["dc.identifier.pmid","25310106"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10992"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/3616"],["dc.notes.intern","mathe"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Public Library of Science (PLoS)"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0/"],["dc.subject","Transportation Algorithms Computer and information sciences Consortia Optimization Approximation methods Imaging techniques Simulation and modeling"],["dc.title","The Shortlist Method for Fast Computation of the Earth Mover's Distance and Finding Optimal Solutions to Transportation Problems"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","International Journal of Health Geographics"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","Konstantinoudis, Garyfallos"],["dc.contributor.author","Schuhmacher, Dominic"],["dc.contributor.author","Ammann, Roland A."],["dc.contributor.author","Diesch, Tamara"],["dc.contributor.author","Kuehni, Claudia E."],["dc.contributor.author","Spycher, Ben D."],["dc.date.accessioned","2020-12-10T18:38:59Z"],["dc.date.available","2020-12-10T18:38:59Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1186/s12942-020-00211-7"],["dc.identifier.eissn","1476-072X"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17225"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77501"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Fakultät für Mathematik und Informatik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Bayesian spatial modelling of childhood cancer incidence in Switzerland using exact point data: a nationwide study during 1985–2015"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]