{"id":"https://openalex.org/W4406461037","doi":"https://doi.org/10.1109/bigdata62323.2024.10825663","title":"Random Forest-Supervised Manifold Alignment","display_name":"Random Forest-Supervised Manifold Alignment","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406461037","doi":"https://doi.org/10.1109/bigdata62323.2024.10825663"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825663","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825663","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5041155337","display_name":"Jake S. Rhodes","orcid":"https://orcid.org/0009-0006-3306-1696"},"institutions":[{"id":"https://openalex.org/I100005738","display_name":"Brigham Young University","ror":"https://ror.org/047rhhm47","country_code":"US","type":"education","lineage":["https://openalex.org/I100005738"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jake S. Rhodes","raw_affiliation_strings":["Brigham Young University,Department of Statistics,Provo,Utah,USA"],"affiliations":[{"raw_affiliation_string":"Brigham Young University,Department of Statistics,Provo,Utah,USA","institution_ids":["https://openalex.org/I100005738"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114634034","display_name":"Adam G. Rustad","orcid":null},"institutions":[{"id":"https://openalex.org/I100005738","display_name":"Brigham Young University","ror":"https://ror.org/047rhhm47","country_code":"US","type":"education","lineage":["https://openalex.org/I100005738"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adam G. Rustad","raw_affiliation_strings":["Brigham Young University,Department of Computer Science,Provo,Utah,USA"],"affiliations":[{"raw_affiliation_string":"Brigham Young University,Department of Computer Science,Provo,Utah,USA","institution_ids":["https://openalex.org/I100005738"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5041155337"],"corresponding_institution_ids":["https://openalex.org/I100005738"],"apc_list":null,"apc_paid":null,"fwci":0.5165,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.68643406,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"3309","last_page":"3312"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.998199999332428,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.998199999332428,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9980999827384949,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9976000189781189,"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/random-forest","display_name":"Random forest","score":0.712710440158844},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5803002119064331},{"id":"https://openalex.org/keywords/manifold","display_name":"Manifold (fluid mechanics)","score":0.5116551518440247},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4150926470756531},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32437437772750854},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.32351619005203247},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.26535236835479736},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10025456547737122}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.712710440158844},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5803002119064331},{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.5116551518440247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4150926470756531},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32437437772750854},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.32351619005203247},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.26535236835479736},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10025456547737122},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825663","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825663","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1500583457","https://openalex.org/W2047081748","https://openalex.org/W2108759471","https://openalex.org/W2123261262","https://openalex.org/W2767290858","https://openalex.org/W2954148993","https://openalex.org/W2978997241","https://openalex.org/W2979573534","https://openalex.org/W2985076077","https://openalex.org/W2992779234","https://openalex.org/W2993894543","https://openalex.org/W4213367101","https://openalex.org/W4255865707","https://openalex.org/W4256060553","https://openalex.org/W4303647085","https://openalex.org/W4308307852","https://openalex.org/W4361982189","https://openalex.org/W4361988503","https://openalex.org/W4388235533","https://openalex.org/W4388235560","https://openalex.org/W4400762160","https://openalex.org/W4404342930","https://openalex.org/W6600880057","https://openalex.org/W6607781037","https://openalex.org/W6749111886","https://openalex.org/W6753329009","https://openalex.org/W6763509818","https://openalex.org/W6874374889"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Manifold":[0],"alignment":[1,37,46,60,121],"is":[2],"a":[3,11,42,77,95,163],"type":[4],"of":[5,15],"data":[6,16,170],"fusion":[7],"technique":[8],"that":[9,84,109,120,123,146,158],"creates":[10],"shared":[12],"low-dimensional":[13],"representation":[14],"collected":[17],"from":[18,70],"multiple":[19,143],"domains,":[20],"enabling":[21],"cross-domain":[22,82,151],"learning":[23],"and":[24,154],"improved":[25,132],"performance":[26],"in":[27,98],"downstream":[28,114,135],"tasks.":[29],"This":[30],"paper":[31],"presents":[32],"an":[33],"approach":[34,93],"to":[35,106],"manifold":[36,99],"using":[38],"random":[39,71,125,159],"forests":[40],"as":[41,76],"foundation":[43],"for":[44,80,113,166],"semi-supervised":[45],"algorithms,":[47],"leveraging":[48],"the":[49],"model\u2019s":[50],"inherent":[51],"strengths.":[52],"We":[53],"focus":[54],"on":[55,134],"enhancing":[56],"two":[57],"recently":[58],"developed":[59],"graph-based":[61],"by":[62],"integrating":[63],"class":[64],"labels":[65],"through":[66],"geometry-preserving":[67],"proximities":[68,74,127,161],"derived":[69],"forests.":[72],"These":[73],"serve":[75],"supervised":[78],"initialization":[79],"constructing":[81],"relationships":[83],"maintain":[85],"local":[86],"neighborhood":[87],"structures,":[88],"thereby":[89],"facilitating":[90],"alignment.":[91,171],"Our":[92],"addresses":[94],"common":[96],"limitation":[97],"alignment,":[100],"where":[101],"existing":[102],"methods":[103],"often":[104],"fail":[105],"generate":[107],"embeddings":[108],"capture":[110],"sufficient":[111],"information":[112,130],"classification.":[115],"By":[116],"contrast,":[117],"we":[118],"find":[119],"models":[122],"use":[124],"forest":[126,160],"or":[128],"class-label":[129],"achieve":[131],"accuracy":[133],"classification":[136],"tasks,":[137],"outperforming":[138],"single-domain":[139],"baselines.":[140],"Experiments":[141],"across":[142],"datasets":[144],"show":[145],"our":[147],"method":[148],"typically":[149],"enhances":[150],"feature":[152],"integration":[153],"predictive":[155],"performance,":[156],"suggesting":[157],"offer":[162],"practical":[164],"solution":[165],"tasks":[167],"requiring":[168],"multimodal":[169]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}
