{"id":"https://openalex.org/W2087815488","doi":"https://doi.org/10.1109/icmlc.2013.6890365","title":"Robust principal curves based on maximum correntropy criterion","display_name":"Robust principal curves based on maximum correntropy criterion","publication_year":2013,"publication_date":"2013-07-01","ids":{"openalex":"https://openalex.org/W2087815488","doi":"https://doi.org/10.1109/icmlc.2013.6890365","mag":"2087815488"},"language":"en","primary_location":{"id":"doi:10.1109/icmlc.2013.6890365","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2013.6890365","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 International Conference on Machine Learning and Cybernetics","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102331774","display_name":"Chun-Guo Li","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I43337087","display_name":"Hebei University","ror":"https://ror.org/01p884a79","country_code":"CN","type":"education","lineage":["https://openalex.org/I43337087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chun-Guo Li","raw_affiliation_strings":["Machine Learning Center, Hebei University, Baoding, China","NLPR/LIAMA, Chinese Academy of Sciences, Beijing, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Learning Center, Hebei University, Baoding, China","institution_ids":["https://openalex.org/I43337087"]},{"raw_affiliation_string":"NLPR/LIAMA, Chinese Academy of Sciences, Beijing, P.R. China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019329695","display_name":"Bao-Gang Hu","orcid":"https://orcid.org/0000-0002-6916-5394"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bao-Gang Hu","raw_affiliation_strings":["NLPR/LIAMA, Chinese Academy of Sciences, Beijing, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NLPR/LIAMA, Chinese Academy of Sciences, Beijing, P.R. China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16650974,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"615","last_page":"620"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9983999729156494,"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/T10320","display_name":"Neural Networks and Applications","score":0.9983999729156494,"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/T10057","display_name":"Face and Expression Recognition","score":0.9801999926567078,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.975600004196167,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7511379718780518},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7142935991287231},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6679645776748657},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5131226181983948},{"id":"https://openalex.org/keywords/curve-fitting","display_name":"Curve fitting","score":0.4929072856903076},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4709794521331787},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.43953824043273926},{"id":"https://openalex.org/keywords/principal-axis-theorem","display_name":"Principal axis theorem","score":0.41785550117492676},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41599154472351074},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39848506450653076},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20731201767921448},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.07707789540290833}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7511379718780518},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7142935991287231},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6679645776748657},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5131226181983948},{"id":"https://openalex.org/C184389593","wikidata":"https://www.wikidata.org/wiki/Q603159","display_name":"Curve fitting","level":2,"score":0.4929072856903076},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4709794521331787},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43953824043273926},{"id":"https://openalex.org/C161326058","wikidata":"https://www.wikidata.org/wiki/Q7245073","display_name":"Principal axis theorem","level":2,"score":0.41785550117492676},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41599154472351074},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39848506450653076},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20731201767921448},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.07707789540290833},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icmlc.2013.6890365","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2013.6890365","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 International Conference on Machine Learning and Cybernetics","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.715.5890","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.715.5890","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.escience.cn/system/file?fileId%3D70070","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/12","display_name":"Responsible consumption and production"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320326873","display_name":"National Laboratory of Pattern Recognition","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1506295442","https://openalex.org/W1663973292","https://openalex.org/W1968440166","https://openalex.org/W1981554428","https://openalex.org/W2025640367","https://openalex.org/W2029305623","https://openalex.org/W2042383323","https://openalex.org/W2071128523","https://openalex.org/W2103633133","https://openalex.org/W2109857912","https://openalex.org/W2112604397","https://openalex.org/W2113713615","https://openalex.org/W2125027820","https://openalex.org/W2135160607","https://openalex.org/W2137823674","https://openalex.org/W2138675231","https://openalex.org/W2141050848","https://openalex.org/W2141759154","https://openalex.org/W2157191463","https://openalex.org/W2166329520","https://openalex.org/W2168951963","https://openalex.org/W2294798173","https://openalex.org/W2378115875","https://openalex.org/W4212863985","https://openalex.org/W4230367971","https://openalex.org/W4249667877","https://openalex.org/W4251002338","https://openalex.org/W6630159191","https://openalex.org/W6676047094","https://openalex.org/W6995580487"],"related_works":["https://openalex.org/W1975632186","https://openalex.org/W3027745756","https://openalex.org/W3205213561","https://openalex.org/W2531880140","https://openalex.org/W2126145365","https://openalex.org/W2036609560","https://openalex.org/W346861917","https://openalex.org/W3024018414","https://openalex.org/W1542592062","https://openalex.org/W4250644203"],"abstract_inverted_index":{"Principal":[0,34],"curves":[1,3],"are":[2,14],"which":[4],"pass":[5],"through":[6],"the":[7,48,57,60,89,92],"`middle\u2032":[8],"of":[9,18,53,78,91],"a":[10,26],"data":[11,19,79],"cloud.":[12],"They":[13],"sensitive":[15],"to":[16,74],"variances":[17,75],"clouds.":[20,80],"In":[21],"this":[22],"paper,":[23],"we":[24],"propose":[25],"robust":[27],"principal":[28,49,97],"curve":[29,50],"model":[30,46,62,71,94],"-":[31],"Correntropy":[32],"based":[33,38],"Curve":[35],"(CPC)":[36],"model,":[37],"on":[39,83],"maximum":[40],"correntropy":[41],"criterion":[42],"(MCC).":[43],"The":[44,69],"CPC":[45,61,70,93],"approximate":[47],"with":[51],"k-segments":[52],"polygonal":[54],"line.":[55],"Employing":[56],"half-quadratic":[58],"technique,":[59],"is":[63,72],"optimized":[64],"in":[65,95],"an":[66],"iteratively":[67],"way.":[68],"insensitive":[73],"and":[76,85],"outliers":[77],"Extensive":[81],"experiments":[82],"synthetic":[84],"real-life":[86],"datasets":[87],"illustrate":[88],"robustness":[90],"learning":[96],"curves.":[98]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
