{"id":"https://openalex.org/W4221145162","doi":"https://doi.org/10.48550/arxiv.2203.14810","title":"Data-Driven, Soft Alignment of Functional Data Using Shapes and Landmarks","display_name":"Data-Driven, Soft Alignment of Functional Data Using Shapes and Landmarks","publication_year":2022,"publication_date":"2022-03-22","ids":{"openalex":"https://openalex.org/W4221145162","doi":"https://doi.org/10.48550/arxiv.2203.14810"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2203.14810","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.14810","pdf_url":"https://arxiv.org/pdf/2203.14810","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2203.14810","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047496267","display_name":"Xiaoyang Guo","orcid":"https://orcid.org/0000-0003-0259-137X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Guo, Xiaoyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052928277","display_name":"Wei Wu","orcid":"https://orcid.org/0000-0003-3938-8359"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Wei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5086786635","display_name":"Anuj Srivastava","orcid":"https://orcid.org/0000-0001-7406-0338"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Srivastava, Anuj","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5047496267"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12417","display_name":"Morphological variations and asymmetry","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12417","display_name":"Morphological variations and asymmetry","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9713000059127808,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.947700023651123,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/landmark","display_name":"Landmark","score":0.9342338442802429},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.643919050693512},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5557929277420044},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5437437891960144},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5294163823127747},{"id":"https://openalex.org/keywords/extension","display_name":"Extension (predicate logic)","score":0.5191863775253296},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5044649839401245},{"id":"https://openalex.org/keywords/fisher-information","display_name":"Fisher information","score":0.45710501074790955},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43304207921028137},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41940316557884216},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.41781583428382874},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3385201096534729},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3211378753185272},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2916157841682434},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.13011062145233154},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09733361005783081},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0783626139163971}],"concepts":[{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.9342338442802429},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.643919050693512},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5557929277420044},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5437437891960144},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5294163823127747},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.5191863775253296},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5044649839401245},{"id":"https://openalex.org/C29406490","wikidata":"https://www.wikidata.org/wiki/Q1420659","display_name":"Fisher information","level":2,"score":0.45710501074790955},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43304207921028137},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41940316557884216},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41781583428382874},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3385201096534729},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3211378753185272},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2916157841682434},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.13011062145233154},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09733361005783081},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0783626139163971},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2203.14810","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.14810","pdf_url":"https://arxiv.org/pdf/2203.14810","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2203.14810","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2203.14810","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2203.14810","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.14810","pdf_url":"https://arxiv.org/pdf/2203.14810","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7599999904632568}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2056853153","https://openalex.org/W2057559274","https://openalex.org/W2005087563","https://openalex.org/W2378111931","https://openalex.org/W2052388267","https://openalex.org/W2950647290","https://openalex.org/W1968481813","https://openalex.org/W2620829895","https://openalex.org/W2356918560","https://openalex.org/W4243161226"],"abstract_inverted_index":{"Alignment":[0],"or":[1],"registration":[2],"of":[3,12,58,75,100],"functions":[4,13,33,135],"is":[5,51,70,141],"a":[6,22,83,105,115,130],"fundamental":[7],"problem":[8],"in":[9,67,73,114,146],"statistical":[10],"analysis":[11],"and":[14,30,65,110,136],"shapes.":[15],"While":[16],"there":[17,69],"are":[18],"several":[19],"approaches":[20],"available,":[21],"more":[23],"recent":[24],"approach":[25],"based":[26],"on":[27],"Fisher-Rao":[28],"metric":[29],"square-root":[31],"velocity":[32],"(SRVFs)":[34],"has":[35,46],"been":[36],"shown":[37],"to":[38,53,85,103,128,143],"have":[39],"good":[40],"performance.":[41],"However,":[42],"this":[43,90],"SRVF":[44],"method":[45,140],"two":[47],"limitations:":[48],"(1)":[49],"it":[50],"susceptible":[52],"over":[54],"alignment,":[55],"i.e.,":[56],"alignment":[57,118],"noise":[59],"as":[60,62],"well":[61],"the":[63,77],"signal,":[64],"(2)":[66],"case":[68],"additional":[71],"information":[72,102],"form":[74],"landmarks,":[76],"original":[78],"formulation":[79],"does":[80],"not":[81],"prescribe":[82],"way":[84],"incorporate":[86],"that":[87,96,119],"information.":[88],"In":[89],"paper":[91],"we":[92],"propose":[93],"an":[94],"extension":[95],"allows":[97],"for":[98],"incorporation":[99],"landmark":[101,117],"seek":[104],"compromise":[106,131],"between":[107,132],"matching":[108],"curves":[109],"landmarks.":[111,137],"This":[112],"results":[113],"soft":[116],"pushes":[120],"landmarks":[121],"closer,":[122],"without":[123],"requiring":[124],"their":[125],"exact":[126],"overlays":[127],"finds":[129],"contributions":[133],"from":[134],"The":[138],"proposed":[139],"demonstrated":[142],"be":[144],"superior":[145],"certain":[147],"practical":[148],"scenarios.":[149]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2022-04-03T00:00:00"}