{"id":"https://openalex.org/W3214289837","doi":"https://doi.org/10.1145/3474717.3483949","title":"Fr\u00e9chet Kernel for Trajectory Data Analysis","display_name":"Fr\u00e9chet Kernel for Trajectory Data Analysis","publication_year":2021,"publication_date":"2021-11-02","ids":{"openalex":"https://openalex.org/W3214289837","doi":"https://doi.org/10.1145/3474717.3483949","mag":"3214289837"},"language":"en","primary_location":{"id":"doi:10.1145/3474717.3483949","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3474717.3483949","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3474717.3483949","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3474717.3483949","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021174309","display_name":"Koh Takeuchi","orcid":"https://orcid.org/0000-0002-6227-4627"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Koh Takeuchi","raw_affiliation_strings":["Kyoto University, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kyoto University, Kyoto, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066308746","display_name":"Masaaki Imaizumi","orcid":"https://orcid.org/0000-0001-6186-613X"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masaaki Imaizumi","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021315429","display_name":"S Kanda","orcid":"https://orcid.org/0000-0002-5462-122X"},"institutions":[{"id":"https://openalex.org/I4210126580","display_name":"RIKEN Center for Advanced Intelligence Project","ror":"https://ror.org/03ckxwf91","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652","https://openalex.org/I4210126580"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shunsuke Kanda","raw_affiliation_strings":["RIKEN AIP Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN AIP Tokyo, Japan","institution_ids":["https://openalex.org/I4210126580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021642801","display_name":"Yasuo Tabei","orcid":"https://orcid.org/0000-0003-2368-5607"},"institutions":[{"id":"https://openalex.org/I4210126580","display_name":"RIKEN Center for Advanced Intelligence Project","ror":"https://ror.org/03ckxwf91","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652","https://openalex.org/I4210126580"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yasuo Tabei","raw_affiliation_strings":["RIKEN AIP Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN AIP Tokyo, Japan","institution_ids":["https://openalex.org/I4210126580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025530694","display_name":"Keisuke Fujii","orcid":"https://orcid.org/0000-0001-5487-4297"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Keisuke Fujii","raw_affiliation_strings":["Nagoya University, Nagoya, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya University, Nagoya, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054366504","display_name":"Ken Yoda","orcid":"https://orcid.org/0000-0002-8346-3291"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ken Yoda","raw_affiliation_strings":["Nagoya University, Nagoya, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya University, Nagoya, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025599257","display_name":"Masakazu Ishihata","orcid":"https://orcid.org/0000-0003-0971-060X"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masakazu Ishihata","raw_affiliation_strings":["NTT Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039456378","display_name":"Takuya Maekawa","orcid":"https://orcid.org/0000-0002-7227-580X"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takuya Maekawa","raw_affiliation_strings":["Osaka University, Osaka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Osaka University, Osaka, Japan","institution_ids":["https://openalex.org/I98285908"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"221","last_page":"224"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.994700014591217,"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"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/robustness","display_name":"Robustness (evolution)","score":0.7168130874633789},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6792246103286743},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6379163265228271},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6169134974479675},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5531396865844727},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5180816650390625},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49485892057418823},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4526611566543579},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4292854368686676},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36497360467910767},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11466792225837708}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7168130874633789},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6792246103286743},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6379163265228271},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6169134974479675},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5531396865844727},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5180816650390625},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49485892057418823},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4526611566543579},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4292854368686676},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36497360467910767},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11466792225837708},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3474717.3483949","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3474717.3483949","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3474717.3483949","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3474717.3483949","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3474717.3483949","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3474717.3483949","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1936760464","display_name":null,"funder_award_id":"21H05293","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G3407190576","display_name":"Easily available information technology based on the data-driven models for social biomechanics","funder_award_id":"20H04075","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G4069792685","display_name":"Developing a non-asymptotic inference theory for scientific hypothesis testing with statistical deep modelling","funder_award_id":"21K11780","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G8885344818","display_name":null,"funder_award_id":"21H05300","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3214289837.pdf","grobid_xml":"https://content.openalex.org/works/W3214289837.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W116902681","https://openalex.org/W1494959908","https://openalex.org/W1521536236","https://openalex.org/W1809001740","https://openalex.org/W2059432181","https://openalex.org/W2084404970","https://openalex.org/W2096394554","https://openalex.org/W2118529802","https://openalex.org/W2121770144","https://openalex.org/W2124299914","https://openalex.org/W2124958607","https://openalex.org/W2126194848","https://openalex.org/W2129857356","https://openalex.org/W2131076549","https://openalex.org/W2135098562","https://openalex.org/W2136317921","https://openalex.org/W2143325592","https://openalex.org/W2154818210","https://openalex.org/W2160754664","https://openalex.org/W2189007323","https://openalex.org/W2580962517","https://openalex.org/W2734775449","https://openalex.org/W2795016801","https://openalex.org/W2810365191","https://openalex.org/W2810764694","https://openalex.org/W2913368959","https://openalex.org/W2919115771","https://openalex.org/W2951870359","https://openalex.org/W2952559136","https://openalex.org/W2963001155","https://openalex.org/W2963945905","https://openalex.org/W3029579534","https://openalex.org/W3090747022","https://openalex.org/W6678573052"],"related_works":["https://openalex.org/W4323768008","https://openalex.org/W1941703695","https://openalex.org/W3131574667","https://openalex.org/W4360995134","https://openalex.org/W4248382324","https://openalex.org/W3023605104","https://openalex.org/W2039473718","https://openalex.org/W2387529410","https://openalex.org/W2383578611","https://openalex.org/W1996690921"],"abstract_inverted_index":{"Trajectory":[0],"analysis":[1],"has":[2,42,117],"been":[3],"a":[4,19,50,87,92,118],"central":[5],"problem":[6],"in":[7,152],"applications":[8],"of":[9,26,30,94],"location":[10],"tracking":[11],"systems.":[12],"Recently,":[13],"the":[14,24,39,79,128],"(discrete)":[15],"Fr\u00e9chet":[16,40,80,89],"distance":[17,41,90],"becomes":[18],"popular":[20],"approach":[21],"for":[22,52],"measuring":[23],"similarity":[25],"two":[27,95],"trajectories":[28,106],"because":[29],"its":[31,37,53,70],"high":[32,54],"feature":[33,55],"extraction":[34,56,103],"capability.":[35],"Despite":[36],"importance,":[38],"several":[43],"limitations:":[44],"(i)":[45],"sensitive":[46],"to":[47,69,110,137],"noise":[48],"as":[49],"trade-off":[51],"capability;":[57],"and":[58,149],"(ii)":[59],"it":[60],"cannot":[61],"be":[62,125],"incorporated":[63,126],"into":[64,127],"machine":[65],"learning":[66],"frameworks":[67],"due":[68],"non-smooth":[71],"functions.":[72],"To":[73],"address":[74],"these":[75],"problems,":[76],"we":[77,113],"propose":[78],"kernel":[81,129,147],"(FRK),":[82],"which":[83],"is":[84],"associated":[85],"with":[86],"smoothed":[88],"using":[91],"combination":[93],"approximation":[96],"techniques.":[97],"FRK":[98,116,123,141],"can":[99,124],"adaptively":[100],"acquire":[101],"appropriate":[102],"capability":[104],"from":[105],"while":[107],"retaining":[108],"robustness":[109],"noise.":[111],"Theoretically,":[112],"find":[114],"that":[115],"positive":[119],"definite":[120],"property,":[121],"hence":[122],"method.":[130],"We":[131],"also":[132],"provide":[133],"an":[134],"efficient":[135],"algorithm":[136],"calculate":[138],"FRK.":[139],"Experimentally,":[140],"outperforms":[142],"other":[143,146],"methods,":[144],"including":[145],"methods":[148],"neural":[150],"networks,":[151],"various":[153],"noisy":[154],"real-data":[155],"classification":[156],"tasks.":[157]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}