{"id":"https://openalex.org/W2773235229","doi":"https://doi.org/10.1109/smc.2017.8122623","title":"Autoencoder using kernel methoc","display_name":"Autoencoder using kernel methoc","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2773235229","doi":"https://doi.org/10.1109/smc.2017.8122623","mag":"2773235229"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2017.8122623","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2017.8122623","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5020451338","display_name":"Yan Pei","orcid":"https://orcid.org/0000-0003-1545-9204"},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yan Pei","raw_affiliation_strings":["Computer Science Division University of Aizu, Fukushima, Aizu-wakamatsu, Japan 965-8580"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Division University of Aizu, Fukushima, Aizu-wakamatsu, Japan 965-8580","institution_ids":["https://openalex.org/I141591182"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5020451338"],"corresponding_institution_ids":["https://openalex.org/I141591182"],"apc_list":null,"apc_paid":null,"fwci":0.2768,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.57398328,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"322","last_page":"327"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9993000030517578,"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":0.9993000030517578,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9993000030517578,"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":0.9969000220298767,"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/autoencoder","display_name":"Autoencoder","score":0.950035572052002},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.7865438461303711},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.7786437273025513},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.721198558807373},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6860899329185486},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6713710427284241},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.5572110414505005},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.5094287991523743},{"id":"https://openalex.org/keywords/principal-component-regression","display_name":"Principal component regression","score":0.5000793933868408},{"id":"https://openalex.org/keywords/tree-kernel","display_name":"Tree kernel","score":0.49711015820503235},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49504318833351135},{"id":"https://openalex.org/keywords/radial-basis-function-kernel","display_name":"Radial basis function kernel","score":0.4882211983203888},{"id":"https://openalex.org/keywords/abstraction","display_name":"Abstraction","score":0.4447599947452545},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.4202161729335785},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35292214155197144},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3261624574661255},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.29725536704063416},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16765525937080383},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.15589556097984314}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.950035572052002},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.7865438461303711},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7786437273025513},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.721198558807373},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6860899329185486},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6713710427284241},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.5572110414505005},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.5094287991523743},{"id":"https://openalex.org/C74887250","wikidata":"https://www.wikidata.org/wiki/Q3455892","display_name":"Principal component regression","level":3,"score":0.5000793933868408},{"id":"https://openalex.org/C140417398","wikidata":"https://www.wikidata.org/wiki/Q16933942","display_name":"Tree kernel","level":5,"score":0.49711015820503235},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49504318833351135},{"id":"https://openalex.org/C75866337","wikidata":"https://www.wikidata.org/wiki/Q7280263","display_name":"Radial basis function kernel","level":4,"score":0.4882211983203888},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.4447599947452545},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.4202161729335785},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35292214155197144},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3261624574661255},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.29725536704063416},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16765525937080383},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.15589556097984314},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc.2017.8122623","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2017.8122623","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1498436455","https://openalex.org/W1510073064","https://openalex.org/W1574672153","https://openalex.org/W1584444527","https://openalex.org/W2021405695","https://openalex.org/W2100495367","https://openalex.org/W2101036899","https://openalex.org/W2103212315","https://openalex.org/W2133665775","https://openalex.org/W2136922672","https://openalex.org/W2137983211","https://openalex.org/W2140095548","https://openalex.org/W2145889472","https://openalex.org/W2147800946","https://openalex.org/W2167608136","https://openalex.org/W2294798173","https://openalex.org/W2787894218","https://openalex.org/W2919115771","https://openalex.org/W3146803896","https://openalex.org/W6634336115","https://openalex.org/W6634927326","https://openalex.org/W6684918892"],"related_works":["https://openalex.org/W3013206934","https://openalex.org/W3100948281","https://openalex.org/W4311138679","https://openalex.org/W3123056048","https://openalex.org/W1983263273","https://openalex.org/W4291669689","https://openalex.org/W2071590642","https://openalex.org/W1969447452","https://openalex.org/W2005311689","https://openalex.org/W2393746448"],"abstract_inverted_index":{"We":[0,115,148],"propose":[1],"a":[2,94,159],"method":[3,38,104],"that":[4,44,127],"uses":[5],"kernel":[6,37,142,162],"method-based":[7,163],"algorithms":[8,15],"to":[9,119],"implement":[10,106],"an":[11],"autoencoder.":[12],"Deep":[13],"learning-based":[14],"have":[16],"two":[17],"characteristics,":[18],"one":[19,40,56],"is":[20,28,39,93],"the":[21,26,29,42,58,64,70,77,102,137,141],"high":[22],"level":[23,31],"data":[24,32,118],"abstraction,":[25],"other":[27],"multiple":[30],"transformations":[33,62],"and":[34,50,73,86,97,110,132,144,151,156],"representations.":[35],"The":[36,124],"of":[41,57,60,76,161],"approaches":[43],"can":[45,105,130],"be":[46,55],"used":[47],"in":[48,63,99],"linear":[49,88],"non-linear":[51],"transformations.":[52],"It":[53],"should":[54],"implementations":[59],"these":[61],"deep":[65,100,107,164],"learning.":[66,165],"In":[67],"this":[68],"paper,":[69],"encoder":[71],"part":[72,75],"decoder":[74],"autoencoder":[78,92,129],"are":[79],"implemented":[80],"by":[81],"kernel-based":[82,87,128],"principal":[83],"component":[84],"analysis":[85],"regression,":[89],"respectively.":[90],"As":[91],"basic":[95],"structure":[96],"algorithm":[98,111],"learning,":[101],"proposed":[103,122],"learning":[108],"model":[109],"using":[112],"duplicate":[113],"structures.":[114],"use":[116],"image":[117,134],"evaluate":[120],"our":[121],"method.":[123],"results":[125],"show":[126],"represent":[131],"restore":[133],"data,":[135],"but":[136],"performance":[138],"depends":[139],"on":[140],"function":[143],"its":[145],"parameters'":[146],"selection.":[147],"also":[149],"discuss":[150],"analyse":[152],"some":[153],"open":[154],"topics":[155],"works":[157],"towards":[158],"study":[160]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
