{"id":"https://openalex.org/W2040909647","doi":"https://doi.org/10.1145/2072298.2072027","title":"Combining latent semantic learning and reduced hypergraph learning for semi-supervised image categorization","display_name":"Combining latent semantic learning and reduced hypergraph learning for semi-supervised image categorization","publication_year":2011,"publication_date":"2011-11-28","ids":{"openalex":"https://openalex.org/W2040909647","doi":"https://doi.org/10.1145/2072298.2072027","mag":"2040909647"},"language":"en","primary_location":{"id":"doi:10.1145/2072298.2072027","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2072298.2072027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM international conference on Multimedia","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/A5085349794","display_name":"Zhiwu Lu","orcid":"https://orcid.org/0000-0003-0280-7724"},"institutions":[{"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":true,"raw_author_name":"Zhiwu Lu","raw_affiliation_strings":["Peking University, Beijing, China","Peking University, Beijing, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking University, Beijing, China#TAB#","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047811387","display_name":"Yuxin Peng","orcid":"https://orcid.org/0000-0001-7658-3845"},"institutions":[{"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":"Yuxin Peng","raw_affiliation_strings":["Peking University, Beijing, China","Peking University, Beijing, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking University, Beijing, China#TAB#","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5085349794"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.5151,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.68010702,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1409","last_page":"1412"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":1.0,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":1.0,"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":0.9993000030517578,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9979000091552734,"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/computer-science","display_name":"Computer science","score":0.7180315256118774},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6964244842529297},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.6637387275695801},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.6617363095283508},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5723420977592468},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5638834238052368},{"id":"https://openalex.org/keywords/hypergraph","display_name":"Hypergraph","score":0.5272659063339233},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5108332633972168},{"id":"https://openalex.org/keywords/probabilistic-latent-semantic-analysis","display_name":"Probabilistic latent semantic analysis","score":0.48966386914253235},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48219290375709534},{"id":"https://openalex.org/keywords/latent-semantic-analysis","display_name":"Latent semantic analysis","score":0.4454942047595978},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4279436469078064},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4144553542137146},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4095754623413086},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1426449418067932}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7180315256118774},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6964244842529297},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6637387275695801},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.6617363095283508},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5723420977592468},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5638834238052368},{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.5272659063339233},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5108332633972168},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.48966386914253235},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48219290375709534},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.4454942047595978},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4279436469078064},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4144553542137146},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4095754623413086},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1426449418067932},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2072298.2072027","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2072298.2072027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM international conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8199999928474426,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1566135517","https://openalex.org/W1589362500","https://openalex.org/W1999182238","https://openalex.org/W2005676288","https://openalex.org/W2107034620","https://openalex.org/W2154455818","https://openalex.org/W2155161883","https://openalex.org/W2162915993","https://openalex.org/W2165846633","https://openalex.org/W2165874743","https://openalex.org/W2170057991","https://openalex.org/W2171896402"],"related_works":["https://openalex.org/W2588002110","https://openalex.org/W2111020819","https://openalex.org/W2775171027","https://openalex.org/W4389358025","https://openalex.org/W2267563544","https://openalex.org/W2303774322","https://openalex.org/W1690254038","https://openalex.org/W2890241594","https://openalex.org/W4295564123","https://openalex.org/W1551384396"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,29,39,60,72],"novel":[4],"framework":[5,95],"that":[6,92],"can":[7,33,54,96],"combine":[8],"latent":[9,35,52],"semantic":[10],"learning":[11,15,76],"and":[12,82],"reduced":[13,73],"hypergraph":[14],"for":[16,85],"semi-supervised":[17,75],"image":[18,86],"categorization.":[19,87],"To":[20],"improve":[21],"the":[22,93,103,106],"traditional":[23],"bag-of-features":[24],"representation,":[25],"we":[26,69],"first":[27],"propose":[28],"semantics-aware":[30,67],"representation":[31],"which":[32],"learn":[34],"semantics":[36,53],"automatically":[37],"from":[38],"large":[40],"vocabulary":[41],"of":[42,105],"abundant":[43],"visual":[44],"keywords":[45],"through":[46],"contextual":[47],"spectral":[48],"embedding.":[49],"The":[50],"learnt":[51],"be":[55],"readily":[56],"used":[57],"to":[58,78,102],"define":[59],"histogram":[61],"intersection":[62],"kernel.":[63],"Based":[64],"on":[65],"this":[66],"kernel,":[68],"further":[70],"develop":[71],"hypergraph-based":[74],"method":[77],"exploit":[79],"both":[80],"labeled":[81],"unlabeled":[83],"images":[84],"Experimental":[88],"results":[89],"have":[90],"shown":[91],"proposed":[94],"achieve":[97],"significant":[98],"improvements":[99],"with":[100],"respect":[101],"state":[104],"arts.":[107]},"counts_by_year":[{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}