{"id":"https://openalex.org/W2133294540","doi":"https://doi.org/10.1145/1143844.1143909","title":"Local distance preservation in the GP-LVM through back constraints","display_name":"Local distance preservation in the GP-LVM through back constraints","publication_year":2006,"publication_date":"2006-01-01","ids":{"openalex":"https://openalex.org/W2133294540","doi":"https://doi.org/10.1145/1143844.1143909","mag":"2133294540"},"language":"en","primary_location":{"id":"doi:10.1145/1143844.1143909","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1143844.1143909","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd international conference on Machine learning - ICML '06","raw_type":"proceedings-article"},"type":"conference-paper","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/A5023849469","display_name":"Neil D. Lawrence","orcid":"https://orcid.org/0000-0001-9258-1030"},"institutions":[{"id":"https://openalex.org/I91136226","display_name":"University of Sheffield","ror":"https://ror.org/05krs5044","country_code":"GB","type":"education","lineage":["https://openalex.org/I91136226"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Neil D. Lawrence","raw_affiliation_strings":["[University of Sheffield, Sheffield, U.K]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"[University of Sheffield, Sheffield, U.K]","institution_ids":["https://openalex.org/I91136226"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050604937","display_name":"Joaquin Qui\u00f1onero-Candela","orcid":null},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Joaquin Qui\u00f1onero-Candela","raw_affiliation_strings":["Technical University of Berlin, Berlin (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Berlin, Berlin (Germany)","institution_ids":["https://openalex.org/I4577782"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":223,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"513","last_page":"520"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9995999932289124,"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":0.9916999936103821,"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/T10320","display_name":"Neural Networks and Applications","score":0.9884999990463257,"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.6258050799369812},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6095675230026245},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5486302971839905},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5445802211761475},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5284281373023987},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.5241784453392029},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5199719667434692},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5104550123214722},{"id":"https://openalex.org/keywords/latent-variable-model","display_name":"Latent variable model","score":0.4928922653198242},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.4545941650867462},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.44932985305786133},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4421735107898712},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.40107622742652893},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38958054780960083},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37972715497016907},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.323633074760437}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6258050799369812},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6095675230026245},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5486302971839905},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5445802211761475},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5284281373023987},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.5241784453392029},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5199719667434692},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5104550123214722},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.4928922653198242},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.4545941650867462},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.44932985305786133},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4421735107898712},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.40107622742652893},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38958054780960083},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37972715497016907},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.323633074760437},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1145/1143844.1143909","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1143844.1143909","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd international conference on Machine learning - ICML '06","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.124.2767","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.2767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://swt.cs.tu-berlin.de/~joaquin/papers/lawrence06bcgplvm.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.78.4302","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.78.4302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.icml2006.org/icml_documents/camera-ready/065_Local_Distance_Prese.pdf","raw_type":"text"},{"id":"pmh:oai:fraunhofer.de:N-44589","is_oa":false,"landing_page_url":"http://publica.fraunhofer.de/documents/N-44589.html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400801","display_name":"Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Fraunhofer FIRST","raw_type":"Conference Paper"},{"id":"pmh:oai:publica.fraunhofer.de:publica/351160","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/351160","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1978499618","https://openalex.org/W1982372699","https://openalex.org/W1993845689","https://openalex.org/W2001141328","https://openalex.org/W2017588182","https://openalex.org/W2026915078","https://openalex.org/W2053186076","https://openalex.org/W2097944344","https://openalex.org/W2103510282","https://openalex.org/W2107628931","https://openalex.org/W2107636931","https://openalex.org/W2118057916","https://openalex.org/W2124609748","https://openalex.org/W2125027820","https://openalex.org/W2129813427","https://openalex.org/W2134312057","https://openalex.org/W2136111243","https://openalex.org/W2140095548","https://openalex.org/W2146610201","https://openalex.org/W2148694408","https://openalex.org/W2157444450","https://openalex.org/W2166063021"],"related_works":["https://openalex.org/W1995622179","https://openalex.org/W1484111231","https://openalex.org/W4391160746","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W3089231081","https://openalex.org/W2156897583"],"abstract_inverted_index":{"The":[0],"Gaussian":[1],"process":[2],"latent":[3,23,68,79],"variable":[4],"model":[5],"(GP-LVM)":[6],"is":[7,28],"a":[8,18,30,64],"generative":[9],"approach":[10],"to":[11,24,44,69,122],"nonlinear":[12],"low":[13],"dimensional":[14],"embedding,":[15],"that":[16,81],"provides":[17],"smooth":[19,65],"probabilistic":[20,34],"mapping":[21,66],"from":[22,67],"data":[25,54,70,86,133],"space.":[26,87],"It":[27],"also":[29],"non-linear":[31,45],"generalization":[32],"of":[33,96,106],"PCA":[35],"(PPCA)":[36],"(Tipping":[37],"&amp;amp;":[38],"Bishop,":[39],"1999).":[40],"While":[41],"most":[42],"approaches":[43],"dimensionality":[46,97],"methods":[47],"focus":[48],"on":[49,59,74,103,131],"preserving":[50],"local":[51,125],"distances":[52],"in":[53,78,85],"space,":[55,71],"the":[56,61,101,104,114],"GP-LVM":[57,115],"focusses":[58,73],"exactly":[60],"opposite.":[62],"Being":[63],"it":[72],"keeping":[75],"things":[76],"apart":[77,84],"space":[80],"are":[82],"far":[83],"In":[88],"this":[89],"paper":[90],"we":[91],"first":[92],"provide":[93],"an":[94],"overview":[95],"reduction":[98],"techniques,":[99],"placing":[100],"emphasis":[102],"kind":[105],"distance":[107],"relation":[108],"preserved.":[109],"We":[110,127],"then":[111],"show":[112],"how":[113],"can":[116],"be":[117],"generalized,":[118],"through":[119],"back":[120],"constraints,":[121],"additionally":[123],"preserve":[124],"distances.":[126],"give":[128],"illustrative":[129],"experiments":[130],"common":[132],"sets.":[134],"1.":[135]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":12},{"year":2017,"cited_by_count":12},{"year":2016,"cited_by_count":17},{"year":2015,"cited_by_count":14},{"year":2014,"cited_by_count":12},{"year":2013,"cited_by_count":17},{"year":2012,"cited_by_count":11}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}
