{"id":154105,"date":"2005-10-01T00:00:00","date_gmt":"2005-10-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/metric-embeddings-with-relaxed-guarantees-2\/"},"modified":"2018-10-16T20:22:45","modified_gmt":"2018-10-17T03:22:45","slug":"metric-embeddings-with-relaxed-guarantees-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/metric-embeddings-with-relaxed-guarantees-2\/","title":{"rendered":"Metric Embeddings with Relaxed Guarantees"},"content":{"rendered":"<p>We consider the problem of embedding finite metrics with slack: we seek to produce embeddings with small dimension and distortion while allowing a (small) constant fraction of all distances to be arbitrarily distorted. This definition is motivated by recent research in the networking community, which achieved striking empirical success at embedding Internet latencies with low distortion into low-dimensional Euclidean space, provided that some small slack is allowed. Answering an open question of Kleinberg, Slivkins, and Wexler [29], we show that provable guarantees of this type can in fact be achieved in general: any finite metric can be embedded, with constant slack and constant distortion, into constant-dimensional Euclidean space. We then show that there exist stronger embeddings into `1 which exhibit gracefully degrading distortion: these is a single embedding into `1 that achieves distortion at most O(log 1) on all but at most an \u000f fraction of distances, simultaneously for all \u000f > 0. We extend this with distortion O(log 1)1\/p to maps into general `p, p \u0015 1 for several classes of metrics, including those with bounded doubling dimension and those arising from the shortest-path metric of a graph with an excluded minor. Finally, we show that many of our constructions are tight, and give a general technique to obtain lower bounds for \u000f-slack embeddings from lower bounds for low-distortion embeddings.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We consider the problem of embedding finite metrics with slack: we seek to produce embeddings with small dimension and distortion while allowing a (small) constant fraction of all distances to be arbitrarily distorted. This definition is motivated by recent research in the networking community, which achieved striking empirical success at embedding Internet latencies with low [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Institute of Electrical and Electronics Engineers, Inc.","msr_publisher_other":"","msr_booktitle":"45th IEEE Symp. on Foundations of Computer Science (FOCS)","msr_chapter":"","msr_edition":"45th IEEE Symp. on Foundations of Computer Science (FOCS)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"\u00a9 2007 IEEE. Personal use of this material is permitted. However, permission to reprint\/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.","msr_conference_name":"45th IEEE Symp. on Foundations of Computer Science (FOCS)","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Yair Bartal, Hubert T.-C. 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