{"id":"https://openalex.org/W3205785530","doi":null,"title":"NNK-Means: Dictionary Learning using Non-Negative Kernel regression.","display_name":"NNK-Means: Dictionary Learning using Non-Negative Kernel regression.","publication_year":2021,"publication_date":"2021-10-15","ids":{"openalex":"https://openalex.org/W3205785530","mag":"3205785530"},"language":"en","primary_location":{"id":"mag:3205785530","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2110.08212.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2110.08212.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051816570","display_name":"Sarath Shekkizhar","orcid":"https://orcid.org/0000-0001-6736-0092","observed_orcids":["https://orcid.org/0000-0001-6736-0092"]},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sarath Shekkizhar","raw_affiliation_strings":["University  of Southern California"],"raw_orcid":"https://orcid.org/0000-0001-6736-0092","affiliations":[{"raw_affiliation_string":"University  of Southern California","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040001106","display_name":"Antonio Ortega","orcid":"https://orcid.org/0000-0001-5403-0940","observed_orcids":["https://orcid.org/0000-0001-5403-0940"]},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Antonio Ortega","raw_affiliation_strings":["University  of Southern California"],"raw_orcid":"https://orcid.org/0000-0001-5403-0940","affiliations":[{"raw_affiliation_string":"University  of Southern California","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"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":0.7099999785423279,"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":0.7099999785423279,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.0568000003695488,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.04170000180602074,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/dictionary-learning","display_name":"dictionary learning","score":0.9509999752044678},{"id":"https://openalex.org/keywords/data-summarization","display_name":"data summarization","score":0.9150000214576721},{"id":"https://openalex.org/keywords/k-means-clustering","display_name":"K-means clustering","score":0.652999997138977},{"id":"https://openalex.org/keywords/k-svd","display_name":"K-SVD","score":0.8830000162124634},{"id":"https://openalex.org/keywords/scalable-clustering","display_name":"scalable clustering","score":0.5569999814033508}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9033032655715942},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.736345648765564},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6567223072052002},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5634755492210388},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4912998080253601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4837675094604492},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4577634930610657},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.45339563488960266},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4313369393348694},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37287867069244385},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3571181297302246},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3042905926704407},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12507364153862},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.11943072080612183},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11084344983100891},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06618529558181763},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3205785530","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2110.08212.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3205785530","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2110.08212.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1128809682","https://openalex.org/W1594924988","https://openalex.org/W1927762903","https://openalex.org/W1963932623","https://openalex.org/W1986007546","https://openalex.org/W1992405901","https://openalex.org/W2004465977","https://openalex.org/W2010161441","https://openalex.org/W2011430131","https://openalex.org/W2014158063","https://openalex.org/W2049633694","https://openalex.org/W2051926224","https://openalex.org/W2100659887","https://openalex.org/W2112796928","https://openalex.org/W2115429828","https://openalex.org/W2115549875","https://openalex.org/W2142848040","https://openalex.org/W2149861038","https://openalex.org/W2150593711","https://openalex.org/W2160547390","https://openalex.org/W2168029744","https://openalex.org/W2185231601","https://openalex.org/W2230030897","https://openalex.org/W2504108613","https://openalex.org/W2543892465","https://openalex.org/W2593864460","https://openalex.org/W2776050171","https://openalex.org/W2800431972","https://openalex.org/W2913399920","https://openalex.org/W3015965491","https://openalex.org/W3034978746","https://openalex.org/W3118608800","https://openalex.org/W3197638206"],"related_works":["https://openalex.org/W4312577076","https://openalex.org/W2757944643","https://openalex.org/W4401480763","https://openalex.org/W2787398805","https://openalex.org/W2914985201","https://openalex.org/W2050460554","https://openalex.org/W2027807074","https://openalex.org/W4242624138","https://openalex.org/W4412554339","https://openalex.org/W1927762903"],"abstract_inverted_index":{"An":[0],"increasing":[1],"number":[2],"of":[3,13,69,112,128],"systems":[4],"are":[5],"being":[6],"designed":[7],"by":[8],"first":[9],"gathering":[10],"significant":[11],"amounts":[12],"data,":[14],"and":[15,40,109,114],"then":[16],"optimizing":[17],"the":[18,23,32,67,93],"system":[19],"parameters":[20,41],"directly":[21],"using":[22,101],"obtained":[24],"data.":[25],"Often":[26],"this":[27,57],"is":[28,51],"done":[29],"without":[30],"analyzing":[31],"dataset":[33],"structure.":[34],"As":[35],"task":[36],"complexity,":[37],"data":[38,49,61,95],"size,":[39],"all":[42],"increase":[43],"to":[44,107,126],"millions":[45],"or":[46],"even":[47],"billions,":[48],"summarization":[50,62],"becoming":[52],"a":[53,119],"major":[54],"challenge.":[55],"In":[56],"work,":[58],"we":[59],"investigate":[60],"via":[63],"dictionary":[64],"learning,":[65],"leveraging":[66],"properties":[68],"recently":[70],"introduced":[71],"non-negative":[72],"kernel":[73,110],"regression":[74],"(NNK)":[75],"graphs.":[76],"Our":[77],"proposed":[78],"NNK-Means,":[79],"unlike":[80],"competing":[81],"techniques,":[82],"such":[83],"askSVD,":[84],"learns":[85],"geometric":[86],"dictionaries":[87],"with":[88,122],"atoms":[89],"that":[90,99,127],"lie":[91],"in":[92],"input":[94],"space.":[96],"Experiments":[97],"show":[98],"summaries":[100],"NNK-Meanscan":[102],"provide":[103],"better":[104],"discrimination":[105],"compared":[106],"linear":[108],"versions":[111],"kMeans":[113],"kSVD.":[115],"Moreover,":[116],"NNK-Means":[117],"has":[118],"scalable":[120],"implementation,":[121],"runtime":[123],"complexity":[124],"similar":[125],"kMeans.":[129]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-10-25T00:00:00"}
