{"id":"https://openalex.org/W2767937169","doi":"https://doi.org/10.1109/icdsp.2017.8096068","title":"Sparse subspace clustering using square-root penalty","display_name":"Sparse subspace clustering using square-root penalty","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2767937169","doi":"https://doi.org/10.1109/icdsp.2017.8096068","mag":"2767937169"},"language":"en","primary_location":{"id":"doi:10.1109/icdsp.2017.8096068","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2017.8096068","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 22nd International Conference on Digital Signal Processing (DSP)","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/A5102862973","display_name":"Linghang Meng","orcid":"https://orcid.org/0009-0000-7384-8529"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linghang Meng","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, CHINA","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003439795","display_name":"Xinyue Shen","orcid":"https://orcid.org/0000-0001-7760-0897"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyue Shen","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, CHINA","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100621681","display_name":"Yuantao Gu","orcid":"https://orcid.org/0000-0002-8427-1021"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuantao Gu","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, CHINA","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.247,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57296678,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"14","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9690999984741211,"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/outlier","display_name":"Outlier","score":0.7107632160186768},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6766407489776611},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6647987365722656},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.5922442674636841},{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.5794540643692017},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5475469827651978},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5180373787879944},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4872052073478699},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.48583361506462097},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43985825777053833},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4355519115924835},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4336038827896118},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.43108734488487244},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.402060329914093},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38895183801651},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.2543136477470398}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7107632160186768},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6766407489776611},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6647987365722656},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.5922442674636841},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.5794540643692017},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5475469827651978},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5180373787879944},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4872052073478699},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48583361506462097},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43985825777053833},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4355519115924835},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4336038827896118},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.43108734488487244},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.402060329914093},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38895183801651},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2543136477470398},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdsp.2017.8096068","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2017.8096068","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 22nd International Conference on Digital Signal Processing (DSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W282757658","https://openalex.org/W1993962865","https://openalex.org/W2001141328","https://openalex.org/W2002260165","https://openalex.org/W2003217181","https://openalex.org/W2025391886","https://openalex.org/W2103943817","https://openalex.org/W2132914434","https://openalex.org/W2142782401","https://openalex.org/W2145962650","https://openalex.org/W2147717514","https://openalex.org/W2152461258","https://openalex.org/W2152714163","https://openalex.org/W2164278908","https://openalex.org/W2296616510","https://openalex.org/W2950623019","https://openalex.org/W2951443864","https://openalex.org/W2963605636","https://openalex.org/W3099880660","https://openalex.org/W3121832289","https://openalex.org/W4250955649","https://openalex.org/W4292363360","https://openalex.org/W6675666693"],"related_works":["https://openalex.org/W1980381208","https://openalex.org/W2364594919","https://openalex.org/W2167092671","https://openalex.org/W1861706286","https://openalex.org/W2219338811","https://openalex.org/W2149583853","https://openalex.org/W1995723671","https://openalex.org/W2164647769","https://openalex.org/W2258058088","https://openalex.org/W1826223126"],"abstract_inverted_index":{"We":[0,22],"study":[1],"the":[2,35,39,49,71,78,100,110,117,126,129,136],"sparse":[3,11,20],"subspace":[4],"clustering":[5],"problem":[6,27],"in":[7,85],"presence":[8],"of":[9,61,128],"both":[10],"outliers":[12],"and":[13,38,81,92,97,124],"Gaussian":[14,52],"additive":[15],"noise":[16,137],"based":[17,56],"on":[18,34,57],"data":[19],"self-representation.":[21],"propose":[23],"a":[24,44,68,75],"convex":[25],"optimization":[26],"which":[28],"does":[29],"not":[30],"only":[31],"induce":[32],"sparsity":[33],"representation":[36],"coefficients":[37],"outliers,":[40],"but":[41],"also":[42],"adopts":[43],"square-root":[45],"penalty":[46],"to":[47,135],"improve":[48],"robustness":[50],"against":[51],"noise.":[53],"An":[54],"algorithm":[55,82],"alternating":[58],"direction":[59],"method":[60,119],"multipliers":[62],"(ADMM)":[63],"is":[64,102],"then":[65],"devised":[66],"as":[67],"solver":[69],"for":[70],"proposed":[72,79],"problem.":[73],"As":[74],"real":[76,106],"application,":[77],"model":[80],"are":[83,90],"applied":[84],"motion":[86],"segmentation.":[87],"The":[88],"performances":[89],"demonstrated":[91],"analyzed":[93],"by":[94,104],"synthetic":[95],"data,":[96],"more":[98],"importantly,":[99],"effectiveness":[101],"verified":[103],"some":[105],"data.":[107],"Compared":[108],"with":[109],"reference":[111],"method,":[112],"numerical":[113],"results":[114],"show":[115],"that":[116,125],"new":[118],"achieves":[120],"higher":[121],"cluster":[122],"accuracy":[123],"choice":[127],"parameter":[130],"can":[131],"be":[132],"less":[133],"sensitive":[134],"level.":[138],"<sup":[139],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[140],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[141]},"counts_by_year":[{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}