{"id":"https://openalex.org/W2967066800","doi":"https://doi.org/10.3390/e21080795","title":"Spectral Embedded Deep Clustering","display_name":"Spectral Embedded Deep Clustering","publication_year":2019,"publication_date":"2019-08-15","ids":{"openalex":"https://openalex.org/W2967066800","doi":"https://doi.org/10.3390/e21080795","mag":"2967066800","pmid":"https://pubmed.ncbi.nlm.nih.gov/33267508"},"language":"en","primary_location":{"id":"doi:10.3390/e21080795","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e21080795","pdf_url":"https://www.mdpi.com/1099-4300/21/8/795/pdf?version=1565858153","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/21/8/795/pdf?version=1565858153","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022471954","display_name":"Yuichiro Wada","orcid":"https://orcid.org/0000-0002-5214-1265"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuichiro Wada","raw_affiliation_strings":["Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067450536","display_name":"Shugo Miyamoto","orcid":"https://orcid.org/0000-0002-5080-0837"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shugo Miyamoto","raw_affiliation_strings":["Department of Systems Innovation, School of Engineering, The University of Tokyo, Hongo Campus, Eng. Bldg. No. 3, 2F, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Systems Innovation, School of Engineering, The University of Tokyo, Hongo Campus, Eng. Bldg. No. 3, 2F, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018638815","display_name":"Takumi Nakagama","orcid":null},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takumi Nakagama","raw_affiliation_strings":["Department of Mathematical and Computing Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematical and Computing Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047048710","display_name":"L\u00e9o And\u00e9ol","orcid":"https://orcid.org/0000-0002-8704-4748"},"institutions":[{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"L\u00e9o And\u00e9ol","raw_affiliation_strings":["Computer Science Department, Sorbonne Universit\u00e9, 4 place Jussieu, 75005 Paris, France","RIKEN AIP, Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan"],"raw_orcid":"https://orcid.org/0000-0002-8704-4748","affiliations":[{"raw_affiliation_string":"Computer Science Department, Sorbonne Universit\u00e9, 4 place Jussieu, 75005 Paris, France","institution_ids":["https://openalex.org/I39804081"]},{"raw_affiliation_string":"RIKEN AIP, Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003820265","display_name":"Wataru Kumagai","orcid":"https://orcid.org/0000-0002-3081-5951"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wataru Kumagai","raw_affiliation_strings":["RIKEN AIP, Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN AIP, Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088569462","display_name":"Takafumi Kanamori","orcid":"https://orcid.org/0000-0001-6878-5850"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Takafumi Kanamori","raw_affiliation_strings":["Department of Mathematical and Computing Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan","RIKEN AIP, Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan"],"raw_orcid":"https://orcid.org/0000-0001-6878-5850","affiliations":[{"raw_affiliation_string":"Department of Mathematical and Computing Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan","institution_ids":["https://openalex.org/I114531698"]},{"raw_affiliation_string":"RIKEN AIP, Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5088569462"],"corresponding_institution_ids":["https://openalex.org/I114531698"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.4063,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.65321997,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"21","issue":"8","first_page":"795","last_page":"795"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9991999864578247,"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":0.9991999864578247,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","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"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9958999752998352,"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/cluster-analysis","display_name":"Cluster analysis","score":0.7081542015075684},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6968428492546082},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6624329090118408},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6559289693832397},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6529434323310852},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5960731506347656},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5202797055244446},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4963741898536682},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.45857149362564087},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.4524773359298706},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.4253847002983093},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.42347270250320435}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7081542015075684},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6968428492546082},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6624329090118408},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6559289693832397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6529434323310852},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5960731506347656},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5202797055244446},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4963741898536682},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.45857149362564087},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.4524773359298706},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.4253847002983093},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.42347270250320435},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/e21080795","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e21080795","pdf_url":"https://www.mdpi.com/1099-4300/21/8/795/pdf?version=1565858153","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:33267508","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33267508","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:df8e76ccc40345bba778cdd58568d1a5","is_oa":true,"landing_page_url":"https://doaj.org/article/df8e76ccc40345bba778cdd58568d1a5","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 21, Iss 8, p 795 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/21/8/795/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/e21080795","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:7515324","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7515324","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e21080795","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e21080795","pdf_url":"https://www.mdpi.com/1099-4300/21/8/795/pdf?version=1565858153","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2939306700","display_name":"Theoretical Analysis of Transfer Learning and Its Applications","funder_award_id":"17K12653","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G3304413201","display_name":"Combinatorial designs and their optimalitiy related to Codes, spherical  designs and grouptesting","funder_award_id":"15H03636","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G5372431915","display_name":"Theories and Methodologies for Large Complex Data","funder_award_id":"15H01678","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G5540623307","display_name":null,"funder_award_id":"17K12653; 15H01678; 15H03636; 16K00044; 19H04071","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G7615765131","display_name":"Statistical Learning with feature extraction and information integration  of High-dimensional, large-scale, multi-domain data","funder_award_id":"19H04071","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G8498071073","display_name":"Mathematics and Practical Algorithms for machine Learning methods with non-convex losses","funder_award_id":"16K00044","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W206566442","https://openalex.org/W1479807131","https://openalex.org/W1501500081","https://openalex.org/W1560724230","https://openalex.org/W1601914754","https://openalex.org/W1665214252","https://openalex.org/W1677182931","https://openalex.org/W1945616565","https://openalex.org/W1966502880","https://openalex.org/W2001597875","https://openalex.org/W2011430131","https://openalex.org/W2073459066","https://openalex.org/W2097482982","https://openalex.org/W2099111195","https://openalex.org/W2100495367","https://openalex.org/W2110026675","https://openalex.org/W2112796928","https://openalex.org/W2118148215","https://openalex.org/W2120303002","https://openalex.org/W2125687218","https://openalex.org/W2127218421","https://openalex.org/W2129068307","https://openalex.org/W2132914434","https://openalex.org/W2145494108","https://openalex.org/W2150102617","https://openalex.org/W2153233077","https://openalex.org/W2163605009","https://openalex.org/W2165874743","https://openalex.org/W2178768799","https://openalex.org/W2187089797","https://openalex.org/W2222512263","https://openalex.org/W2337374958","https://openalex.org/W2405933695","https://openalex.org/W2504835324","https://openalex.org/W2550243005","https://openalex.org/W2593814746","https://openalex.org/W2730106296","https://openalex.org/W2751947065","https://openalex.org/W2781711557","https://openalex.org/W2884851420","https://openalex.org/W2903322478","https://openalex.org/W2919115771","https://openalex.org/W2949117887","https://openalex.org/W2950365520","https://openalex.org/W2951004968","https://openalex.org/W2952006246","https://openalex.org/W2962852342","https://openalex.org/W2964074409","https://openalex.org/W2964159205","https://openalex.org/W6677873289","https://openalex.org/W6678975374","https://openalex.org/W6681875376","https://openalex.org/W6684578312","https://openalex.org/W6685380521"],"related_works":["https://openalex.org/W2131828344","https://openalex.org/W2330313492","https://openalex.org/W2985124601","https://openalex.org/W3181754176","https://openalex.org/W3023007226","https://openalex.org/W3005536189","https://openalex.org/W2889369603","https://openalex.org/W2533990316","https://openalex.org/W2759831793","https://openalex.org/W3205251672"],"abstract_inverted_index":{"We":[0,37,190],"propose":[1],"a":[2,8,39,46,51,69,138,161],"new":[3],"clustering":[4,126],"method":[5,22,116,171,179],"based":[6],"on":[7,184,194],"deep":[9,47,129],"neural":[10,48,130],"network.":[11],"Given":[12],"an":[13],"unlabeled":[14,64,91,109],"dataset":[15,26,140],"and":[16,72,88],"the":[17,25,28,34,60,80,84,89,97,102,134,156,177,201,204],"number":[18,30],"of":[19,31,63,106,114,137,203],"clusters,":[20],"our":[21,115,170],"directly":[23],"groups":[24],"into":[27],"given":[29,108,139],"clusters":[32],"in":[33],"original":[35],"space.":[36],"use":[38],"conditional":[40],"discrete":[41],"probability":[42],"distribution":[43],"defined":[44],"by":[45,82,95,160],"network":[49],"as":[50,153,155],"statistical":[52],"model.":[53],"Our":[54],"strategy":[55],"is":[56,117,141,158,167,172,180],"first":[57],"to":[58,74,78,149,182,199],"estimate":[59],"cluster":[61,86,104,135],"labels":[62,87,105],"data":[65,92,110,151,157],"points":[66],"selected":[67],"from":[68],"high-density":[70],"region,":[71],"then":[73],"conduct":[75],"semi-supervised":[76],"learning":[77],"train":[79],"model":[81],"using":[83,96],"estimated":[85,103],"remaining":[90],"points.":[93,111],"Lastly,":[94],"trained":[98],"model,":[99],"we":[100],"obtain":[101],"all":[107],"The":[112],"advantage":[113],"that":[118,133,169],"it":[119,144,166],"does":[120],"not":[121],"require":[122],"key":[123],"conditions.":[124],"Existing":[125],"methods":[127],"with":[128],"networks":[131],"assume":[132],"balance":[136],"uniform.":[142],"Moreover,":[143],"also":[145],"can":[146],"be":[147],"applied":[148],"various":[150],"domains":[152],"long":[154],"expressed":[159],"feature":[162],"vector.":[163],"In":[164],"addition,":[165],"observed":[168],"robust":[173],"against":[174],"outliers.":[175],"Therefore,":[176],"proposed":[178,205],"expected":[181],"perform,":[183],"average,":[185],"better":[186],"than":[187],"previous":[188],"methods.":[189],"conducted":[191],"numerical":[192],"experiments":[193],"five":[195],"commonly":[196],"used":[197],"datasets":[198],"confirm":[200],"effectiveness":[202],"method.":[206]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-24T13:16:06.693445","created_date":"2025-10-10T00:00:00"}
