{"id":"https://openalex.org/W2805616023","doi":"https://doi.org/10.1109/lsp.2018.2843281","title":"Adaptive Matrix Sketching and Clustering for Semisupervised Incremental Learning","display_name":"Adaptive Matrix Sketching and Clustering for Semisupervised Incremental Learning","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2805616023","doi":"https://doi.org/10.1109/lsp.2018.2843281","mag":"2805616023"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2018.2843281","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2018.2843281","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-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/A5101580150","display_name":"Zilin Zhang","orcid":"https://orcid.org/0000-0002-9179-9043"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zilin Zhang","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101533266","display_name":"Yan Li","orcid":"https://orcid.org/0000-0001-9562-9634"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Li","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029826575","display_name":"Zhengwen Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengwen Zhang","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084109465","display_name":"Cheng Jin","orcid":"https://orcid.org/0000-0002-5180-0484"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Jin","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030860096","display_name":"Meiguo Gao","orcid":"https://orcid.org/0000-0003-3651-1710"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meiguo Gao","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101580150"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":1.1402,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.83444511,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"25","issue":"7","first_page":"1069","last_page":"1073"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9998000264167786,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9998000264167786,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9972000122070312,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9686999917030334,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.7757272720336914},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7324130535125732},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6575688719749451},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6116119027137756},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5804871916770935},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.508756697177887},{"id":"https://openalex.org/keywords/incremental-learning","display_name":"Incremental learning","score":0.42298242449760437},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3609614372253418},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3576538562774658}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7757272720336914},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7324130535125732},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6575688719749451},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6116119027137756},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5804871916770935},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.508756697177887},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.42298242449760437},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3609614372253418},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3576538562774658},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2018.2843281","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2018.2843281","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5876998749","display_name":null,"funder_award_id":"2017RC02","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G7424405731","display_name":null,"funder_award_id":"61501033","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W55606816","https://openalex.org/W659510205","https://openalex.org/W1485437584","https://openalex.org/W1522301498","https://openalex.org/W1548280412","https://openalex.org/W1970950689","https://openalex.org/W1992968767","https://openalex.org/W2009155043","https://openalex.org/W2056339660","https://openalex.org/W2062170945","https://openalex.org/W2063484628","https://openalex.org/W2064774462","https://openalex.org/W2065347048","https://openalex.org/W2088424151","https://openalex.org/W2108271008","https://openalex.org/W2108919995","https://openalex.org/W2120250216","https://openalex.org/W2130416896","https://openalex.org/W2149933564","https://openalex.org/W2150159007","https://openalex.org/W2160684493","https://openalex.org/W2165835468","https://openalex.org/W2212076521","https://openalex.org/W2296719434","https://openalex.org/W2405883473","https://openalex.org/W2470412537","https://openalex.org/W2601172051","https://openalex.org/W2604735226","https://openalex.org/W2964121744","https://openalex.org/W4299518610","https://openalex.org/W6602222081","https://openalex.org/W6621935316","https://openalex.org/W6631190155","https://openalex.org/W6632890887","https://openalex.org/W6682132143","https://openalex.org/W6688364005","https://openalex.org/W6719935260","https://openalex.org/W6736270679"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2028665553","https://openalex.org/W4230315250","https://openalex.org/W2086519370","https://openalex.org/W2885226678"],"abstract_inverted_index":{"Semisupervised":[0],"incremental":[1,40,58],"learning":[2,24,86],"is":[3,17,67],"the":[4,22,85,107,119],"task":[5],"of":[6,28,110,114,121],"classifying":[7],"data":[8,13,101],"streams":[9],"with":[10],"partially":[11],"labeled":[12],"when":[14],"annotation":[15],"information":[16,109],"difficult":[18],"to":[19,91],"obtain.":[20],"Besides":[21],"sequential":[23],"manner":[25],"and":[26,34,64,74,83,112,125],"lack":[27],"label":[29],"information,":[30],"multiple":[31,80],"novel":[32,81],"classes":[33],"concept":[35],"drift":[36],"may":[37],"emerge":[38],"from":[39],"learning.":[41],"Most":[42],"previous":[43,130],"studies":[44],"have":[45],"only":[46],"considered":[47],"these":[48],"problems":[49],"in":[50,56,69],"part.":[51],"To":[52],"tackle":[53],"challenges":[54],"involved":[55],"semisupervised":[57],"learning,":[59],"an":[60],"adaptive":[61],"matrix":[62],"sketching":[63],"clustering":[65],"method":[66,94,124],"proposed":[68,123],"this":[70,93],"letter,":[71],"which":[72],"cohesively":[73],"adaptively":[75],"classifies":[76],"known":[77],"classes,":[78,82],"identifies":[79],"updates":[84],"model.":[87],"Experiments":[88],"were":[89],"conducted":[90],"evaluate":[92],"on":[95],"three":[96],"benchmark":[97],"datasets,":[98],"containing":[99],"various":[100],"types,":[102],"including":[103],"network":[104],"attack":[105],"analysis,":[106],"geospatial":[108],"forests,":[111],"images":[113],"handwritten":[115],"numbers.":[116],"Results":[117],"validated":[118],"effectiveness":[120],"our":[122],"its":[126],"superiority":[127],"over":[128],"many":[129],"studies.":[131]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
