{"id":"https://openalex.org/W2118120419","doi":"https://doi.org/10.1109/cvpr.2012.6247944","title":"Non-negative low rank and sparse graph for semi-supervised learning","display_name":"Non-negative low rank and sparse graph for semi-supervised learning","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2118120419","doi":"https://doi.org/10.1109/cvpr.2012.6247944","mag":"2118120419"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2012.6247944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6247944","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Conference on Computer Vision and Pattern Recognition","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/A5044163181","display_name":"Liansheng Zhuang","orcid":"https://orcid.org/0000-0002-4345-856X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liansheng Zhuang","raw_affiliation_strings":["MOE-Microsoft Key Laboratory, University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"MOE-Microsoft Key Laboratory, University of Science and Technology, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052995186","display_name":"Haoyuan Gao","orcid":"https://orcid.org/0000-0002-2402-6817"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haoyuan Gao","raw_affiliation_strings":["MOE-Microsoft Key Laboratory, University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"MOE-Microsoft Key Laboratory, University of Science and Technology, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016399094","display_name":"Zhouchen Lin","orcid":"https://orcid.org/0000-0003-1493-7569"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]},{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhouchen Lin","raw_affiliation_strings":["Key Laboratory of Machine Perception (MOE), Peking University, China","Microsoft Research Asia, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Machine Perception (MOE), Peking University, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Microsoft Research Asia, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101777958","display_name":"Yi Ma","orcid":"https://orcid.org/0000-0002-4476-4673"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Ma","raw_affiliation_strings":["Microsoft Research Asia, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327568","display_name":"Xin Zhang","orcid":"https://orcid.org/0000-0003-1138-7637"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Zhang","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090634503","display_name":"Nenghai Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nenghai Yu","raw_affiliation_strings":["MOE-Microsoft Key Laboratory, University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"MOE-Microsoft Key Laboratory, University of Science and Technology, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5044163181"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":30.7785,"has_fulltext":false,"cited_by_count":322,"citation_normalized_percentile":{"value":0.99878839,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2328","last_page":"2335"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9975000023841858,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9879999756813049,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8057397603988647},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6375217437744141},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6196591854095459},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5920828580856323},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5729013085365295},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.543941080570221},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5167266726493835},{"id":"https://openalex.org/keywords/dense-graph","display_name":"Dense graph","score":0.49948620796203613},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.4627523422241211},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43562692403793335},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.43101412057876587},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.4172933101654053},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29150786995887756},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.19432973861694336},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.11267068982124329},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.09592586755752563}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8057397603988647},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6375217437744141},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6196591854095459},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5920828580856323},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5729013085365295},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.543941080570221},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5167266726493835},{"id":"https://openalex.org/C13251829","wikidata":"https://www.wikidata.org/wiki/Q3085841","display_name":"Dense graph","level":5,"score":0.49948620796203613},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4627523422241211},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43562692403793335},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.43101412057876587},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.4172933101654053},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29150786995887756},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.19432973861694336},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.11267068982124329},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.09592586755752563},{"id":"https://openalex.org/C102192266","wikidata":"https://www.wikidata.org/wiki/Q4545823","display_name":"1-planar graph","level":4,"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/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2012.6247944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6247944","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.675.7627","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.675.7627","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cis.pku.edu.cn/faculty/vision/zlin/Publications/2012-CVPR-NNLRS.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.676.168","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.676.168","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://yima.csl.illinois.edu/psfile/low-rank+sparse-cvpr12.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W38891395","https://openalex.org/W79405465","https://openalex.org/W1736339626","https://openalex.org/W1871180460","https://openalex.org/W1902027874","https://openalex.org/W1904464160","https://openalex.org/W1968555645","https://openalex.org/W2047071281","https://openalex.org/W2069959554","https://openalex.org/W2073946125","https://openalex.org/W2100549954","https://openalex.org/W2103972604","https://openalex.org/W2104290444","https://openalex.org/W2129812935","https://openalex.org/W2133442079","https://openalex.org/W2136504847","https://openalex.org/W2139823104","https://openalex.org/W2141923507","https://openalex.org/W2145962650","https://openalex.org/W2147021384","https://openalex.org/W2154455818","https://openalex.org/W2163584563","https://openalex.org/W2951085447","https://openalex.org/W6603183647","https://openalex.org/W6639239318","https://openalex.org/W6675164516","https://openalex.org/W6680434193","https://openalex.org/W6682494755","https://openalex.org/W6929385289"],"related_works":["https://openalex.org/W2483318309","https://openalex.org/W1566229417","https://openalex.org/W4316252382","https://openalex.org/W1893178273","https://openalex.org/W4299546378","https://openalex.org/W3138826529","https://openalex.org/W4320554512","https://openalex.org/W2542063421","https://openalex.org/W2539871928","https://openalex.org/W2951138411"],"abstract_inverted_index":{"Constructing":[0],"a":[1,24,46,58],"good":[2],"graph":[3,31,41],"to":[4,111],"represent":[5],"data":[6,55],"structures":[7],"is":[8,91],"critical":[9],"for":[10,32],"many":[11],"important":[12],"machine":[13],"learning":[14],"tasks":[15],"such":[16],"as":[17,57],"clustering":[18],"and":[19,28,49,79,94,105],"classification.":[20],"This":[21],"paper":[22],"proposes":[23],"novel":[25],"non-negative":[26],"low-rank":[27,48],"sparse":[29,50],"(NNLRS)":[30],"semi-supervised":[33,103],"learning.":[34],"The":[35,63],"weights":[36],"of":[37,61,72,87,100,115],"edges":[38],"in":[39,102],"the":[40,69,76,80,85,88,98,112],"are":[42],"obtained":[43,119],"by":[44],"seeking":[45],"nonnegative":[47],"matrix":[51],"that":[52],"represents":[53],"each":[54],"sample":[56],"linear":[59,82],"combination":[60],"others.":[62],"so-obtained":[64],"NNLRS-graph":[65,101,116],"can":[66],"capture":[67],"both":[68,92],"global":[70],"mixture":[71],"subspaces":[73],"structure":[74,83],"(by":[75,84],"low":[77],"rankness)":[78],"locally":[81],"sparseness)":[86],"data,":[89],"hence":[90],"generative":[93],"discriminative.":[95],"We":[96],"demonstrate":[97],"effectiveness":[99],"classification":[104],"discriminative":[106],"analysis.":[107],"Extensive":[108],"experiments":[109],"testify":[110],"significant":[113],"advantages":[114],"over":[117],"graphs":[118],"through":[120],"conventional":[121],"means.":[122]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":20},{"year":2020,"cited_by_count":23},{"year":2019,"cited_by_count":33},{"year":2018,"cited_by_count":47},{"year":2017,"cited_by_count":30},{"year":2016,"cited_by_count":42},{"year":2015,"cited_by_count":47},{"year":2014,"cited_by_count":28},{"year":2013,"cited_by_count":11},{"year":2012,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
