{"id":"https://openalex.org/W3216144749","doi":"https://doi.org/10.1007/s10994-021-06108-1","title":"Multiway p-spectral graph cuts on Grassmann manifolds","display_name":"Multiway p-spectral graph cuts on Grassmann manifolds","publication_year":2021,"publication_date":"2021-11-18","ids":{"openalex":"https://openalex.org/W3216144749","doi":"https://doi.org/10.1007/s10994-021-06108-1","mag":"3216144749","pmid":"https://pubmed.ncbi.nlm.nih.gov/35400807"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-021-06108-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-021-06108-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-06108-1.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-06108-1.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029330389","display_name":"Dimosthenis Pasadakis","orcid":"https://orcid.org/0000-0001-8580-1023"},"institutions":[{"id":"https://openalex.org/I57201433","display_name":"Universit\u00e0 della Svizzera italiana","ror":"https://ror.org/03c4atk17","country_code":"CH","type":"education","lineage":["https://openalex.org/I57201433"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Dimosthenis Pasadakis","raw_affiliation_strings":["Institute of Computing, Faculty of Informatics, Universit\u00e0 della Svizzera italiana, Lugano, Switzerland"],"affiliations":[{"raw_affiliation_string":"Institute of Computing, Faculty of Informatics, Universit\u00e0 della Svizzera italiana, Lugano, Switzerland","institution_ids":["https://openalex.org/I57201433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058357790","display_name":"Christie Louis Alappat","orcid":"https://orcid.org/0000-0003-4548-8727"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christie Louis Alappat","raw_affiliation_strings":["Department of Computer Science, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017590193","display_name":"Olaf Schenk","orcid":"https://orcid.org/0000-0001-8636-1023"},"institutions":[{"id":"https://openalex.org/I57201433","display_name":"Universit\u00e0 della Svizzera italiana","ror":"https://ror.org/03c4atk17","country_code":"CH","type":"education","lineage":["https://openalex.org/I57201433"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Olaf Schenk","raw_affiliation_strings":["Institute of Computing, Faculty of Informatics, Universit\u00e0 della Svizzera italiana, Lugano, Switzerland"],"affiliations":[{"raw_affiliation_string":"Institute of Computing, Faculty of Informatics, Universit\u00e0 della Svizzera italiana, Lugano, Switzerland","institution_ids":["https://openalex.org/I57201433"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070209050","display_name":"Gerhard Wellein","orcid":"https://orcid.org/0000-0001-7371-3026"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Gerhard Wellein","raw_affiliation_strings":["Department of Computer Science, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5029330389"],"corresponding_institution_ids":["https://openalex.org/I57201433"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":1.6593,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.86472872,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"111","issue":"2","first_page":"791","last_page":"829"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12541","display_name":"Graph Labeling and Dimension Problems","score":0.9710999727249146,"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"}},"topics":[{"id":"https://openalex.org/T12541","display_name":"Graph Labeling and Dimension Problems","score":0.9710999727249146,"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"}},{"id":"https://openalex.org/T11476","display_name":"Graph theory and applications","score":0.9674999713897705,"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/T10996","display_name":"Computational Geometry and Mesh Generation","score":0.9635999798774719,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/mathematics","display_name":"Mathematics","score":0.557307243347168},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5545981526374817},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.4082210063934326},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35008084774017334},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3385000228881836},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3231927752494812}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.557307243347168},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5545981526374817},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.4082210063934326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35008084774017334},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3385000228881836},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3231927752494812}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1007/s10994-021-06108-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-021-06108-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-06108-1.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},{"id":"pmid:35400807","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35400807","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine learning","raw_type":null},{"id":"pmh:oai:arXiv.org:2008.13210","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.13210","pdf_url":"https://arxiv.org/pdf/2008.13210","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":null,"raw_type":"text"},{"id":"pmh:oai:pubmedcentral.nih.gov:8948154","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8948154","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Mach Learn","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1007/s10994-021-06108-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-021-06108-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-06108-1.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1343135564","display_name":null,"funder_award_id":"200021_182673","funder_id":"https://openalex.org/F4320320924","funder_display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung"}],"funders":[{"id":"https://openalex.org/F4320320924","display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung","ror":"https://ror.org/00yjd3n13"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3216144749.pdf","grobid_xml":"https://content.openalex.org/works/W3216144749.grobid-xml"},"referenced_works_count":92,"referenced_works":["https://openalex.org/W25664821","https://openalex.org/W36680195","https://openalex.org/W40976687","https://openalex.org/W95836444","https://openalex.org/W100944330","https://openalex.org/W113245464","https://openalex.org/W121215922","https://openalex.org/W1483985387","https://openalex.org/W1497494571","https://openalex.org/W1510147702","https://openalex.org/W1520511539","https://openalex.org/W1578099820","https://openalex.org/W1587027143","https://openalex.org/W1587744656","https://openalex.org/W1624035878","https://openalex.org/W1804110266","https://openalex.org/W1966461475","https://openalex.org/W1971758446","https://openalex.org/W1988130573","https://openalex.org/W1993137812","https://openalex.org/W2023655578","https://openalex.org/W2027758045","https://openalex.org/W2029676642","https://openalex.org/W2032285574","https://openalex.org/W2045512849","https://openalex.org/W2049633694","https://openalex.org/W2077119805","https://openalex.org/W2096146461","https://openalex.org/W2103560185","https://openalex.org/W2106435568","https://openalex.org/W2114694099","https://openalex.org/W2121947440","https://openalex.org/W2125531986","https://openalex.org/W2127218421","https://openalex.org/W2127376708","https://openalex.org/W2132914434","https://openalex.org/W2134901644","https://openalex.org/W2135674549","https://openalex.org/W2135957668","https://openalex.org/W2136289064","https://openalex.org/W2136935487","https://openalex.org/W2141376824","https://openalex.org/W2146377359","https://openalex.org/W2152322845","https://openalex.org/W2161455936","https://openalex.org/W2165874743","https://openalex.org/W2186574009","https://openalex.org/W2194321275","https://openalex.org/W2478679892","https://openalex.org/W2494489586","https://openalex.org/W2595697910","https://openalex.org/W2604272474","https://openalex.org/W2615556757","https://openalex.org/W2735310850","https://openalex.org/W2746173003","https://openalex.org/W2798707604","https://openalex.org/W2800411455","https://openalex.org/W2803437104","https://openalex.org/W2809496910","https://openalex.org/W2886739333","https://openalex.org/W2920238557","https://openalex.org/W2937507957","https://openalex.org/W2945955132","https://openalex.org/W2963089591","https://openalex.org/W2963343977","https://openalex.org/W2963964033","https://openalex.org/W2964306489","https://openalex.org/W2981001919","https://openalex.org/W2997891905","https://openalex.org/W3008016835","https://openalex.org/W3034967186","https://openalex.org/W3035397496","https://openalex.org/W3039339554","https://openalex.org/W3120740533","https://openalex.org/W3140094189","https://openalex.org/W3154457244","https://openalex.org/W4205293427","https://openalex.org/W4212774754","https://openalex.org/W4240085642","https://openalex.org/W4241568759","https://openalex.org/W4248478760","https://openalex.org/W4255333822","https://openalex.org/W4255695749","https://openalex.org/W6601476861","https://openalex.org/W6604645962","https://openalex.org/W6604882983","https://openalex.org/W6629017334","https://openalex.org/W6635179827","https://openalex.org/W6638366325","https://openalex.org/W6678976050","https://openalex.org/W6682541512","https://openalex.org/W6741194625"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W1979597421","https://openalex.org/W2007980826","https://openalex.org/W2061531152","https://openalex.org/W3002753104","https://openalex.org/W2077600819","https://openalex.org/W2142036596","https://openalex.org/W2072657027","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Nonlinear":[0],"reformulations":[1],"of":[2,11,50,54,61,79,105,128,158,166,169,180],"the":[3,35,55,62,102,106,114,119,124,148,167,170,178,188],"spectral":[4,31],"clustering":[5,32,144],"method":[6,150],"have":[7],"gained":[8],"a":[9,27,58,74,84],"lot":[10],"recent":[12],"attention":[13],"due":[14],"to":[15,93,186],"their":[16,21],"increased":[17],"numerical":[18,136],"benefits":[19],"and":[20,126,138,163,183],"solid":[22],"mathematical":[23],"background.":[24],"We":[25,122],"present":[26],"novel":[28],"direct":[29],"multiway":[30],"algorithm":[33,130],"in":[34,83,131,156,164,190],"<i>p</i>-norm,":[36],"for":[37,177],"<mml:math":[38],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"><mml:mrow><mml:mi>p</mml:mi>":[39],"<mml:mo>\u2208</mml:mo>":[40],"<mml:mo>(</mml:mo>":[41],"<mml:mn>1</mml:mn>":[42],"<mml:mo>,</mml:mo>":[43],"<mml:mn>2</mml:mn>":[44],"<mml:mo>]</mml:mo></mml:mrow>":[45],"</mml:math>":[46],".":[47],"The":[48,77],"problem":[49,72],"computing":[51],"multiple":[52],"eigenvectors":[53],"graph":[56,64,95,108,160],"<i>p</i>-Laplacian,":[57],"nonlinear":[59],"generalization":[60],"standard":[63],"Laplacian,":[65],"is":[66,81],"recasted":[67],"as":[68,97],"an":[69],"unconstrained":[70],"minimization":[71],"on":[73],"Grassmann":[75],"manifold.":[76],"value":[78],"<i>p</i>":[80,98],"reduced":[82],"pseudocontinuous":[85],"manner,":[86],"promoting":[87],"sparser":[88],"solution":[89,117],"vectors":[90],"that":[91,111,147],"correspond":[92],"optimal":[94],"cuts":[96,109],"approaches":[99],"one.":[100],"Monitoring":[101],"monotonic":[103],"decrease":[104],"balanced":[107,159],"guarantees":[110],"we":[112,174],"obtain":[113],"best":[115],"available":[116],"from":[118],"<i>p</i>-levels":[120],"considered.":[121],"demonstrate":[123,187],"effectiveness":[125],"accuracy":[127,168],"our":[129],"various":[132,142],"artificial":[133],"test-cases.":[134],"Our":[135],"examples":[137],"comparative":[139],"results":[140],"with":[141],"state-of-the-art":[143],"methods":[145],"indicate":[146],"proposed":[149],"obtains":[151],"high":[152],"quality":[153],"clusters":[154],"both":[155],"terms":[157,165],"cut":[161],"metrics":[162],"labelling":[171],"assignment.":[172],"Furthermore,":[173],"conduct":[175],"studies":[176],"classification":[179],"facial":[181],"images":[182],"handwritten":[184],"characters":[185],"applicability":[189],"real-world":[191],"datasets.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2021-12-06T00:00:00"}
