{"id":"https://openalex.org/W3091296558","doi":"https://doi.org/10.1109/icip40778.2020.9191335","title":"Residual Encoder-Decoder Network For Deep Subspace Clustering","display_name":"Residual Encoder-Decoder Network For Deep Subspace Clustering","publication_year":2020,"publication_date":"2020-09-30","ids":{"openalex":"https://openalex.org/W3091296558","doi":"https://doi.org/10.1109/icip40778.2020.9191335","mag":"3091296558"},"language":"en","primary_location":{"id":"doi:10.1109/icip40778.2020.9191335","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9191335","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","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/A5100728666","display_name":"Shuai Yang","orcid":"https://orcid.org/0000-0001-6792-7933"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Yang","raw_affiliation_strings":["Institute of Big Data Technologies, Shenzhen Graduate School, Peking University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Big Data Technologies, Shenzhen Graduate School, Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026086282","display_name":"Wenqi Zhu","orcid":"https://orcid.org/0000-0001-9380-4363"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenqi Zhu","raw_affiliation_strings":["Institute of Big Data Technologies, Shenzhen Graduate School, Peking University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Big Data Technologies, Shenzhen Graduate School, Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056706680","display_name":"Yuesheng Zhu","orcid":"https://orcid.org/0000-0003-2524-6800"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuesheng Zhu","raw_affiliation_strings":["Institute of Big Data Technologies, Shenzhen Graduate School, Peking University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Big Data Technologies, Shenzhen Graduate School, Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.881,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.76811186,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2895","last_page":"2899"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9979000091552734,"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":0.9979000091552734,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9797999858856201,"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/T10860","display_name":"Speech and Audio Processing","score":0.9783999919891357,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.7869176864624023},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.7229357957839966},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6529983282089233},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6301560997962952},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5945276021957397},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5319597721099854},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5292681455612183},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5181692838668823},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.46316349506378174},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.4583037197589874},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4329734742641449},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41118523478507996},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.31945323944091797},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27721741795539856}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7869176864624023},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.7229357957839966},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6529983282089233},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6301560997962952},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5945276021957397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5319597721099854},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5292681455612183},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5181692838668823},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.46316349506378174},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.4583037197589874},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4329734742641449},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41118523478507996},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31945323944091797},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27721741795539856},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip40778.2020.9191335","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9191335","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","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":44,"referenced_works":["https://openalex.org/W79405465","https://openalex.org/W1026270304","https://openalex.org/W1522301498","https://openalex.org/W1532325895","https://openalex.org/W1600471557","https://openalex.org/W1981458038","https://openalex.org/W1993962865","https://openalex.org/W2017441234","https://openalex.org/W2100495367","https://openalex.org/W2103560185","https://openalex.org/W2112796928","https://openalex.org/W2123921160","https://openalex.org/W2125874614","https://openalex.org/W2143915994","https://openalex.org/W2150414161","https://openalex.org/W2160616617","https://openalex.org/W2162833336","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2271840356","https://openalex.org/W2410894298","https://openalex.org/W2520164769","https://openalex.org/W2532206188","https://openalex.org/W2805395174","https://openalex.org/W2909906879","https://openalex.org/W2950621961","https://openalex.org/W2952285266","https://openalex.org/W2962911132","https://openalex.org/W2963365397","https://openalex.org/W2963840432","https://openalex.org/W2964046669","https://openalex.org/W2964121744","https://openalex.org/W2980751901","https://openalex.org/W6603183647","https://openalex.org/W6626481562","https://openalex.org/W6631190155","https://openalex.org/W6648938636","https://openalex.org/W6684050148","https://openalex.org/W6684191040","https://openalex.org/W6694517276","https://openalex.org/W6726381175","https://openalex.org/W6744043827","https://openalex.org/W6752606552","https://openalex.org/W6769790212"],"related_works":["https://openalex.org/W3100286349","https://openalex.org/W2896134808","https://openalex.org/W3172436493","https://openalex.org/W4287164812","https://openalex.org/W2957492749","https://openalex.org/W1887135636","https://openalex.org/W2386063599","https://openalex.org/W1975884855","https://openalex.org/W3213150849","https://openalex.org/W4285605394"],"abstract_inverted_index":{"Subspace":[0,57],"clustering":[1,18,96],"aims":[2],"to":[3,27,63,71],"cluster":[4],"unlabeled":[5],"data":[6,81],"that":[7],"lies":[8],"in":[9,82,92],"a":[10,50,68],"union":[11],"of":[12,38,80,90],"low-dimensional":[13],"linear":[14,30,74],"subspaces.":[15],"Deep":[16],"subspace":[17],"approaches":[19],"based":[20],"on":[21],"auto-encoders":[22],"have":[23],"become":[24],"very":[25],"popular":[26],"learn":[28],"the":[29,36,65,73,78,88],"representation":[31],"coefficients":[32,75],"from":[33],"data.":[34],"However,":[35],"training":[37,93],"current":[39],"deep":[40,56],"methods":[41],"converges":[42],"slowly,":[43],"which":[44],"is":[45],"extremely":[46],"expensive.":[47],"We":[48],"propose":[49],"novel":[51],"Residual":[52],"Encoder-Decoder":[53],"network":[54],"for":[55],"Clustering":[58],"(RED-SC)":[59],"with":[60],"skip-layer":[61],"connections":[62],"accelerate":[64],"convergence,":[66],"using":[67],"new":[69],"strategy":[70],"generate":[72],"by":[76],"learning":[77],"linearity":[79],"multiple":[83],"latent":[84],"spaces.":[85],"Experiments":[86],"show":[87],"superiority":[89],"RED-SC":[91],"efficiency":[94],"and":[95],"accuracy.":[97]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
