{"id":"https://openalex.org/W2057317424","doi":"https://doi.org/10.1109/mlsp.2015.7324338","title":"Ordinal embedding of unweighted kNN graphs via synchronization","display_name":"Ordinal embedding of unweighted kNN graphs via synchronization","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W2057317424","doi":"https://doi.org/10.1109/mlsp.2015.7324338","mag":"2057317424"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2015.7324338","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2015.7324338","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039600886","display_name":"Mihai Cucuringu","orcid":"https://orcid.org/0000-0002-8464-2152"},"institutions":[{"id":"https://openalex.org/I2799798094","display_name":"UCLA Health","ror":"https://ror.org/01d88se56","country_code":"US","type":"funder","lineage":["https://openalex.org/I2799798094"]},{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mihai Cucuringu","raw_affiliation_strings":["UCLA Mathematics Department","UCLA, Mathematics Department#TAB#"],"affiliations":[{"raw_affiliation_string":"UCLA Mathematics Department","institution_ids":["https://openalex.org/I2799798094"]},{"raw_affiliation_string":"UCLA, Mathematics Department#TAB#","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013716690","display_name":"Joseph Woodworth","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]},{"id":"https://openalex.org/I2799798094","display_name":"UCLA Health","ror":"https://ror.org/01d88se56","country_code":"US","type":"funder","lineage":["https://openalex.org/I2799798094"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph Woodworth","raw_affiliation_strings":["UCLA Mathematics Department","UCLA, Mathematics Department#TAB#"],"affiliations":[{"raw_affiliation_string":"UCLA Mathematics Department","institution_ids":["https://openalex.org/I2799798094"]},{"raw_affiliation_string":"UCLA, Mathematics Department#TAB#","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5039600886"],"corresponding_institution_ids":["https://openalex.org/I2799798094","https://openalex.org/I161318765"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.04091681,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"11","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9708999991416931,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9563999772071838,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6785125732421875},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6421325206756592},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5844416618347168},{"id":"https://openalex.org/keywords/euclidean-space","display_name":"Euclidean space","score":0.5543531179428101},{"id":"https://openalex.org/keywords/synchronization","display_name":"Synchronization (alternating current)","score":0.5375342965126038},{"id":"https://openalex.org/keywords/multidimensional-scaling","display_name":"Multidimensional scaling","score":0.5300725698471069},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5269492268562317},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5198367238044739},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4385395646095276},{"id":"https://openalex.org/keywords/vertex","display_name":"Vertex (graph theory)","score":0.4325275123119354},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.42155835032463074},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.4167449474334717},{"id":"https://openalex.org/keywords/euclidean-geometry","display_name":"Euclidean geometry","score":0.4127601385116577},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3425131142139435},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3224322199821472},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31348174810409546},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.1960703432559967},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.18773287534713745},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1797981560230255},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.16085535287857056}],"concepts":[{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6785125732421875},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6421325206756592},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5844416618347168},{"id":"https://openalex.org/C186450821","wikidata":"https://www.wikidata.org/wiki/Q17295","display_name":"Euclidean space","level":2,"score":0.5543531179428101},{"id":"https://openalex.org/C2778562939","wikidata":"https://www.wikidata.org/wiki/Q1298791","display_name":"Synchronization (alternating current)","level":3,"score":0.5375342965126038},{"id":"https://openalex.org/C91682802","wikidata":"https://www.wikidata.org/wiki/Q620538","display_name":"Multidimensional scaling","level":2,"score":0.5300725698471069},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5269492268562317},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5198367238044739},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4385395646095276},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.4325275123119354},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.42155835032463074},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.4167449474334717},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.4127601385116577},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3425131142139435},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3224322199821472},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31348174810409546},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.1960703432559967},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.18773287534713745},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1797981560230255},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.16085535287857056},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp.2015.7324338","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2015.7324338","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W68180256","https://openalex.org/W85455704","https://openalex.org/W1629456515","https://openalex.org/W1973192023","https://openalex.org/W2051327384","https://openalex.org/W2066003352","https://openalex.org/W2075220720","https://openalex.org/W2084809122","https://openalex.org/W2088247287","https://openalex.org/W2097308346","https://openalex.org/W2111239341","https://openalex.org/W2111288402","https://openalex.org/W2112935422","https://openalex.org/W2132914434","https://openalex.org/W2143703915","https://openalex.org/W2152825437","https://openalex.org/W2167482691","https://openalex.org/W2169371330","https://openalex.org/W2975800720","https://openalex.org/W3100598149","https://openalex.org/W4246354968","https://openalex.org/W4297684092","https://openalex.org/W6602719836","https://openalex.org/W6603418435","https://openalex.org/W6636899073"],"related_works":["https://openalex.org/W2610298398","https://openalex.org/W4281992692","https://openalex.org/W2145396827","https://openalex.org/W2279239548","https://openalex.org/W4294294347","https://openalex.org/W63976795","https://openalex.org/W2404658402","https://openalex.org/W164473625","https://openalex.org/W2105228010","https://openalex.org/W122023996"],"abstract_inverted_index":{"We":[0,56,93],"consider":[1],"the":[2,25,31,42,45,76,79,100,110,134],"problem":[3],"of":[4,18,44,65,81,112,130],"embedding":[5],"unweighted,":[6],"directed":[7],"k-nearest":[8,16],"neighbor":[9],"graphs":[10],"in":[11,75,78,109],"low-dimensional":[12],"Euclidean":[13],"space.":[14],"The":[15,124],"neighbors":[17],"each":[19],"vertex":[20],"provide":[21],"ordinal":[22,38],"information":[23],"on":[24,36,70,121],"distances":[26,32],"between":[27],"points,":[28],"but":[29],"not":[30],"themselves.":[33],"Relying":[34],"only":[35],"such":[37],"information,":[39],"we":[40,86],"recover":[41],"coordinates":[43],"points":[46],"up":[47],"to":[48,99,133],"arbitrary":[49],"similarity":[50],"transformations":[51,53],"(rigid":[52],"and":[54,116],"scaling).":[55],"make":[57],"existing":[58],"approaches":[59],"scalable":[60],"by":[61],"using":[62],"an":[63],"instance":[64],"a":[66,89],"local-to-global":[67],"algorithm":[68,107],"based":[69],"group":[71],"synchronization,":[72],"recently":[73,101],"proposed":[74,102],"literature":[77],"context":[80],"sensor":[82],"network":[83],"localization,":[84],"which":[85],"augment":[87],"with":[88],"scale":[90],"synchronization":[91],"step.":[92],"show":[94],"our":[95],"approach":[96],"compares":[97],"favorably":[98],"Local":[103],"Ordinal":[104],"Embedding":[105],"(LOE)":[106],"even":[108],"case":[111],"smaller":[113],"sized":[114],"problems,":[115],"also":[117],"demonstrate":[118],"its":[119],"scalability":[120],"large":[122],"graphs.":[123],"above":[125],"divide-and-conquer":[126],"paradigm":[127],"can":[128],"be":[129],"independent":[131],"interest":[132],"machine":[135],"learning":[136],"community":[137],"when":[138],"tackling":[139],"geometric":[140],"embeddings":[141],"problems.":[142]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}