{"id":"https://openalex.org/W51669927","doi":null,"title":"FastMap, MetricMap, and Landmark MDS are all Nystrom Algorithms","display_name":"FastMap, MetricMap, and Landmark MDS are all Nystrom Algorithms","publication_year":2005,"publication_date":"2005-01-01","ids":{"openalex":"https://openalex.org/W51669927","mag":"51669927"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.135.9822","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.9822","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://research.microsoft.com/~jplatt/nystrom.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5073032636","display_name":"John C. Platt","orcid":"https://orcid.org/0000-0002-5652-5303","observed_orcids":["https://orcid.org/0000-0002-5652-5303"]},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"John Platt","raw_affiliation_strings":["Microsoft"],"raw_orcid":"https://orcid.org/0000-0002-5652-5303","affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5073032636"],"corresponding_institution_ids":["https://openalex.org/I1290206253"],"apc_list":null,"apc_paid":null,"fwci":12.1167,"has_fulltext":false,"cited_by_count":137,"citation_normalized_percentile":{"value":0.98542607,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"15","last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.41359999775886536,"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/T10057","display_name":"Face and Expression Recognition","score":0.41359999775886536,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.3264000117778778,"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/T10792","display_name":"Matrix Theory and Algorithms","score":0.0706000030040741,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/nystrom-approximation","display_name":"Nystr\u00f6m approximation","score":0.8209999799728394},{"id":"https://openalex.org/keywords/fastmap","display_name":"FastMap","score":0.8629999756813049},{"id":"https://openalex.org/keywords/multidimensional-scaling","display_name":"multidimensional scaling","score":0.9810000061988831},{"id":"https://openalex.org/keywords/eigenvector-approximation","display_name":"eigenvector approximation","score":0.8169999718666077},{"id":"https://openalex.org/keywords/eigenvalue-approximation","display_name":"eigenvalue approximation","score":0.8190000057220459},{"id":"https://openalex.org/keywords/kernel-methods","display_name":"kernel methods","score":0.45899999141693115},{"id":"https://openalex.org/keywords/low-rank-approximation","display_name":"low-rank approximation","score":0.6060000061988831}],"concepts":[{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.7496923804283142},{"id":"https://openalex.org/C49533043","wikidata":"https://www.wikidata.org/wiki/Q2268380","display_name":"Local Multipoint Distribution Service","level":3,"score":0.6324177980422974},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5986087322235107},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.5769426226615906},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5683163404464722},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5274909734725952},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4185425639152527},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2139560580253601},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0838266909122467},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.135.9822","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.9822","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://research.microsoft.com/~jplatt/nystrom.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.59.8082","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.59.8082","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.gatsby.ucl.ac.uk/aistats/fullpapers/230.pdf","raw_type":"text"},{"id":"mag:51669927","doi":null,"is_oa":false,"landing_page_url":"https://www.microsoft.com/en-us/research/wp-content/uploads/2005/01/nystrom2.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306419146","display_name":"International Conference on Artificial Intelligence and Statistics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Artificial Intelligence and Statistics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W654914693","https://openalex.org/W1492061079","https://openalex.org/W1667165204","https://openalex.org/W2001141328","https://openalex.org/W2036488048","https://openalex.org/W2041884394","https://openalex.org/W2112545207","https://openalex.org/W2133324003","https://openalex.org/W2139140682","https://openalex.org/W2147152072","https://openalex.org/W2153934661","https://openalex.org/W2156287497","https://openalex.org/W2167081989","https://openalex.org/W2798909945","https://openalex.org/W3139654456"],"related_works":["https://openalex.org/W7101229274","https://openalex.org/W1864542135","https://openalex.org/W2189345037","https://openalex.org/W4396218849","https://openalex.org/W2134201719","https://openalex.org/W1904057952","https://openalex.org/W7096027601","https://openalex.org/W2184156481","https://openalex.org/W2091994914","https://openalex.org/W3207716758"],"abstract_inverted_index":{"This":[0],"paper":[1],"unifies":[2],"the":[3,23,27,37,62,72,89],"mathematical":[4],"foundation":[5],"of":[6,26,31,47],"three":[7,18],"multidimensional":[8],"scaling":[9],"algorithms:":[10],"FastMap,":[11],"MetricMap,":[12],"and":[13,29,43,51,64,85,92],"Landmark":[14],"MDS":[15],"(LMDS).":[16],"All":[17],"algorithms":[19],"are":[20],"based":[21],"on":[22,61],"Nystr\u00f6m":[24,39,74],"approximation":[25,75],"eigenvectors":[28],"eigenvalues":[30],"a":[32],"matrix.":[33],"LMDS":[34,79],"is":[35,80],"applies":[36],"basic":[38,73],"approximation,":[40],"while":[41],"FastMap":[42,84],"MetricMap":[44,86],"use":[45],"generalizations":[46],"Nystr\u00f6m,":[48],"including":[49],"deflation":[50],"using":[52],"more":[53,81,96],"points":[54],"to":[55,100],"establish":[56],"an":[57],"embedding.":[58],"Empirical":[59],"experiments":[60],"Reuters":[63],"Corel":[65],"Image":[66],"Features":[67],"data":[68],"sets":[69],"show":[70],"that":[71],"outperforms":[76],"these":[77],"generalizations:":[78],"accurate":[82,97],"than":[83],"with":[87],"roughly":[88],"same":[90],"computation":[91],"can":[93],"become":[94],"even":[95],"if":[98],"allowed":[99],"be":[101],"slower.":[102],"1":[103]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":10},{"year":2015,"cited_by_count":14},{"year":2014,"cited_by_count":9},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":9}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}