{"id":"https://openalex.org/W4323831223","doi":"https://doi.org/10.1109/iceic57457.2023.10049946","title":"A New 3-D PCA Regression Method for Manifold Dimension Reduction with Image Analysis","display_name":"A New 3-D PCA Regression Method for Manifold Dimension Reduction with Image Analysis","publication_year":2023,"publication_date":"2023-02-05","ids":{"openalex":"https://openalex.org/W4323831223","doi":"https://doi.org/10.1109/iceic57457.2023.10049946"},"language":"en","primary_location":{"id":"doi:10.1109/iceic57457.2023.10049946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceic57457.2023.10049946","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Electronics, Information, and Communication (ICEIC)","raw_type":"proceedings-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/A5100779090","display_name":"Kyung Min Lee","orcid":"https://orcid.org/0000-0001-8995-0448"},"institutions":[{"id":"https://openalex.org/I4210107562","display_name":"Semyung University","ror":"https://ror.org/01d100w34","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210107562"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Kyung Min Lee","raw_affiliation_strings":["Semyung University Organization,School of Computer Science,Jecheon,Korea"],"affiliations":[{"raw_affiliation_string":"Semyung University Organization,School of Computer Science,Jecheon,Korea","institution_ids":["https://openalex.org/I4210107562"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061468707","display_name":"Chi-Ho Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107562","display_name":"Semyung University","ror":"https://ror.org/01d100w34","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210107562"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Chi-Ho Lin","raw_affiliation_strings":["Semyung University Organization,School of Computer Science,Jecheon,Korea"],"affiliations":[{"raw_affiliation_string":"Semyung University Organization,School of Computer Science,Jecheon,Korea","institution_ids":["https://openalex.org/I4210107562"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100779090"],"corresponding_institution_ids":["https://openalex.org/I4210107562"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01501796,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"3"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9987000226974487,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9987000226974487,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9975000023841858,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.7798108458518982},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7444396018981934},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6830043792724609},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6472949981689453},{"id":"https://openalex.org/keywords/nonlinear-dimensionality-reduction","display_name":"Nonlinear dimensionality reduction","score":0.6088230013847351},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5799115896224976},{"id":"https://openalex.org/keywords/intrinsic-dimension","display_name":"Intrinsic dimension","score":0.5544042587280273},{"id":"https://openalex.org/keywords/manifold","display_name":"Manifold (fluid mechanics)","score":0.5327838659286499},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5017695426940918},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.4597689211368561},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.459396094083786},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4531729817390442},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42324405908584595},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4179501533508301},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4031156301498413},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.26150715351104736},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.12458932399749756}],"concepts":[{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.7798108458518982},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7444396018981934},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6830043792724609},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6472949981689453},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.6088230013847351},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5799115896224976},{"id":"https://openalex.org/C30732413","wikidata":"https://www.wikidata.org/wiki/Q17092636","display_name":"Intrinsic dimension","level":3,"score":0.5544042587280273},{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.5327838659286499},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5017695426940918},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4597689211368561},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.459396094083786},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4531729817390442},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42324405908584595},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4179501533508301},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4031156301498413},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26150715351104736},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.12458932399749756},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iceic57457.2023.10049946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceic57457.2023.10049946","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Electronics, Information, and Communication (ICEIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320311687","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64"},{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1901367599","https://openalex.org/W1982715739","https://openalex.org/W2163389298","https://openalex.org/W2295590120","https://openalex.org/W2997837663","https://openalex.org/W3009870706","https://openalex.org/W3176714582","https://openalex.org/W3178601582","https://openalex.org/W4200336056","https://openalex.org/W6639532027"],"related_works":["https://openalex.org/W2383239174","https://openalex.org/W117517268","https://openalex.org/W2931531042","https://openalex.org/W2355395139","https://openalex.org/W1983074540","https://openalex.org/W2132734978","https://openalex.org/W2166963679","https://openalex.org/W3088634662","https://openalex.org/W3003257333","https://openalex.org/W2573981081"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,24,31,35,68,85,91,119],"new":[6],"3-D":[7],"pca":[8],"regression":[9,32,69],"method":[10,22,28],"for":[11,53,99],"manifold":[12,42,70],"dimension":[13,55],"reduction":[14,56],"with":[15],"applications":[16],"to":[17,90],"image":[18,26,59,81],"analysis.":[19],"The":[20],"proposed":[21],"is":[23,103,113,127],"novel":[25],"analysis":[27],"consisting":[29],"of":[30,34,48,51,65,80],"algorithm":[33],"structure":[36,88],"designed":[37],"based":[38],"on":[39],"an":[40,45,66],"improved":[41,104],"3-DPCA":[43],"and":[44,84,109],"autoencoder":[46],"capable":[47],"nonlinear":[49],"expansion":[50],"PCA":[52],"efficient":[54],"in":[57,129],"large-capacity":[58],"data":[60],"input.":[61],"With":[62],"the":[63,74],"configuration":[64],"autoencoder,":[67],"3-DPCA,":[71],"which":[72],"derives":[73],"best":[75],"hyperplane":[76],"through":[77,115],"three-dimensional":[78],"rotation":[79],"pixel":[82],"values,":[83],"Bayesian":[86],"rule":[87],"similar":[89],"deep":[92,131],"learning":[93],"structure,":[94],"are":[95],"applied.":[96],"Conduct":[97],"experiments":[98],"performance":[100,111],"verification.":[101],"Image":[102],"using":[105],"fine":[106],"dust":[107],"image,":[108],"accuracy":[110],"evaluation":[112],"performed":[114],"classification":[116],"model.":[117],"As":[118],"result,":[120],"it":[121,126],"can":[122],"be":[123],"confirmed":[124],"that":[125],"effective":[128],"performing":[130],"learning.":[132]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
