{"id":"https://openalex.org/W4410874834","doi":"https://doi.org/10.1177/1088467x251344924","title":"Kernel clustering-based deep model pruning for few category datasets","display_name":"Kernel clustering-based deep model pruning for few category datasets","publication_year":2025,"publication_date":"2025-05-30","ids":{"openalex":"https://openalex.org/W4410874834","doi":"https://doi.org/10.1177/1088467x251344924"},"language":"en","primary_location":{"id":"doi:10.1177/1088467x251344924","is_oa":false,"landing_page_url":"https://doi.org/10.1177/1088467x251344924","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis: An International Journal","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/A5101875205","display_name":"Yuchen Huang","orcid":"https://orcid.org/0000-0002-1537-4924"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuchen Huang","raw_affiliation_strings":["School of Rail Transportation, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Rail Transportation, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083785964","display_name":"Jie Xie","orcid":"https://orcid.org/0000-0002-4710-4275"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jie Xie","raw_affiliation_strings":["School of Rail Transportation, Soochow University, Suzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-4710-4275","affiliations":[{"raw_affiliation_string":"School of Rail Transportation, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000407074","display_name":"Kangwei Wang","orcid":"https://orcid.org/0000-0002-7634-3945"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kangwei Wang","raw_affiliation_strings":["School of Rail Transportation, Soochow University, Suzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-7634-3945","affiliations":[{"raw_affiliation_string":"School of Rail Transportation, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082280574","display_name":"Cheng Wu","orcid":"https://orcid.org/0000-0001-5451-3045"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Wu","raw_affiliation_strings":["School of Rail Transportation, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Rail Transportation, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5000407074","https://openalex.org/A5083785964"],"corresponding_institution_ids":["https://openalex.org/I3923682"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05441669,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"30","issue":"2","first_page":"296","last_page":"316"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"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/T10028","display_name":"Topic Modeling","score":0.9991999864578247,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9991000294685364,"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/pruning","display_name":"Pruning","score":0.6928760409355164},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6360421180725098},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6226435303688049},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5914186239242554},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5089190006256104},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.484638512134552},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.42073696851730347},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.09104672074317932},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.07647565007209778},{"id":"https://openalex.org/keywords/botany","display_name":"Botany","score":0.06755444407463074}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6928760409355164},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6360421180725098},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6226435303688049},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5914186239242554},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5089190006256104},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.484638512134552},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42073696851730347},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.09104672074317932},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.07647565007209778},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.06755444407463074}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1177/1088467x251344924","is_oa":false,"landing_page_url":"https://doi.org/10.1177/1088467x251344924","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis: An International Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1526230118","display_name":null,"funder_award_id":"62301353","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4529983604","display_name":null,"funder_award_id":"62401383","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6600684821","display_name":null,"funder_award_id":"62275186","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2560647685","https://openalex.org/W2626107033","https://openalex.org/W2883111419","https://openalex.org/W2924515500","https://openalex.org/W2928560789","https://openalex.org/W2937229771","https://openalex.org/W2963145730","https://openalex.org/W2963150697","https://openalex.org/W2963446712","https://openalex.org/W2965862774","https://openalex.org/W2970958999","https://openalex.org/W2991372685","https://openalex.org/W3014641072","https://openalex.org/W3034251466","https://openalex.org/W3034513523","https://openalex.org/W3035467254","https://openalex.org/W3193895134","https://openalex.org/W4205252812","https://openalex.org/W4224918215","https://openalex.org/W4312796067","https://openalex.org/W4372346067","https://openalex.org/W4372348474","https://openalex.org/W4375869065","https://openalex.org/W4379137329","https://openalex.org/W4386066058","https://openalex.org/W4386076400","https://openalex.org/W4386076493","https://openalex.org/W4386076569","https://openalex.org/W4387870780","https://openalex.org/W4390872453","https://openalex.org/W4390874483","https://openalex.org/W4390874575","https://openalex.org/W4392203579","https://openalex.org/W4392502214","https://openalex.org/W4394596804"],"related_works":["https://openalex.org/W2373300491","https://openalex.org/W2395294869","https://openalex.org/W2378744544","https://openalex.org/W2594301978","https://openalex.org/W2379704676","https://openalex.org/W1998810860","https://openalex.org/W4206442282","https://openalex.org/W2384505857","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Recent":[0],"deep":[1,123],"model":[2,137],"pruning":[3,20,49,151],"methods":[4],"predominantly":[5],"focus":[6],"on":[7,85,127],"large-scale":[8],"datasets":[9,130],"and":[10,145],"typically":[11],"require":[12],"finetuning":[13,31],"before":[14],"deployment.":[15],"However,":[16],"in":[17,65,107],"real-world":[18],"applications,":[19],"is":[21,72],"often":[22],"necessary":[23],"for":[24,80,93,116],"scenarios":[25],"with":[26,131],"fewer":[27],"classification":[28,133],"categories,":[29,134],"where":[30],"must":[32],"be":[33,102],"avoided":[34],"to":[35,75],"preserve":[36],"the":[37,108,114,120,140],"model\u2019s":[38],"generalization":[39,121],"ability.":[40],"To":[41],"address":[42],"these":[43,86],"challenges,":[44],"we":[45],"introduce":[46],"a":[47,63,66,77],"novel":[48],"method":[50,144],"called":[51],"Cluster-based":[52],"Redundancy":[53],"Elimination":[54],"(CRE).":[55],"Specifically,":[56],"CRE":[57],"represents":[58],"each":[59,81],"convolutional":[60,82],"kernel":[61],"as":[62,95],"point":[64,89],"high-dimensional":[67,109],"space.":[68,110],"A":[69],"distance-based":[70],"strategy":[71],"then":[73],"used":[74],"compute":[76],"clustering":[78],"radius":[79],"layer.":[83],"Based":[84],"radii,":[87],"core":[88],"filters":[90,106],"are":[91],"selected":[92],"pruning,":[94],"they":[96],"represent":[97],"redundant":[98],"information":[99],"that":[100],"can":[101],"captured":[103],"by":[104],"neighboring":[105],"This":[111],"approach":[112],"eliminates":[113],"need":[115],"finetuning,":[117],"thus":[118],"preserving":[119],"of":[122,142],"models.":[124],"Extensive":[125],"experiments":[126],"five":[128],"benchmark":[129],"limited":[132],"across":[135],"multiple":[136],"architectures,":[138],"demonstrate":[139],"effectiveness":[141],"our":[143],"its":[146],"superiority":[147],"over":[148],"several":[149],"state-of-the-art":[150],"techniques.":[152]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
