{"id":"https://openalex.org/W7151226517","doi":"https://doi.org/10.1109/icmla66185.2025.00010","title":"Learning Robust Simplex Sparse Representation Using Alternating Linearized Minimization Method","display_name":"Learning Robust Simplex Sparse Representation Using Alternating Linearized Minimization Method","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7151226517","doi":"https://doi.org/10.1109/icmla66185.2025.00010"},"language":null,"primary_location":{"id":"doi:10.1109/icmla66185.2025.00010","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00010","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","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/A5133071969","display_name":"Zhennan Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I167576493","display_name":"Colorado School of Mines","ror":"https://ror.org/04raf6v53","country_code":"US","type":"education","lineage":["https://openalex.org/I167576493"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhennan Shi","raw_affiliation_strings":["Colorado School of Mines,Department of Computer Science,Golden,CO,USA"],"affiliations":[{"raw_affiliation_string":"Colorado School of Mines,Department of Computer Science,Golden,CO,USA","institution_ids":["https://openalex.org/I167576493"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021675280","display_name":"Qiushi Wei","orcid":"https://orcid.org/0000-0001-6820-768X"},"institutions":[{"id":"https://openalex.org/I167576493","display_name":"Colorado School of Mines","ror":"https://ror.org/04raf6v53","country_code":"US","type":"education","lineage":["https://openalex.org/I167576493"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qiushi Wei","raw_affiliation_strings":["Colorado School of Mines,Department of Computer Science,Golden,CO,USA"],"affiliations":[{"raw_affiliation_string":"Colorado School of Mines,Department of Computer Science,Golden,CO,USA","institution_ids":["https://openalex.org/I167576493"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5133071969"],"corresponding_institution_ids":["https://openalex.org/I167576493"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.74891661,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"25","last_page":"32"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.5694000124931335,"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":0.5694000124931335,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.10499999672174454,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.054099999368190765,"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/minification","display_name":"Minification","score":0.5580000281333923},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.5550000071525574},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.42419999837875366},{"id":"https://openalex.org/keywords/simplex-algorithm","display_name":"Simplex algorithm","score":0.37599998712539673},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.34139999747276306},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.33070001006126404}],"concepts":[{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.5580000281333923},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.5550000071525574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48559999465942383},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44749999046325684},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42419999837875366},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.413100004196167},{"id":"https://openalex.org/C144521790","wikidata":"https://www.wikidata.org/wiki/Q134164","display_name":"Simplex algorithm","level":3,"score":0.37599998712539673},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34360000491142273},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.34139999747276306},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.33070001006126404},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3278000056743622},{"id":"https://openalex.org/C62438384","wikidata":"https://www.wikidata.org/wiki/Q331350","display_name":"Simplex","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2709999978542328},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla66185.2025.00010","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00010","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","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":19,"referenced_works":["https://openalex.org/W1504886279","https://openalex.org/W1991359796","https://openalex.org/W2027792236","https://openalex.org/W2027982384","https://openalex.org/W2076786123","https://openalex.org/W2121947440","https://openalex.org/W2124847261","https://openalex.org/W2127284416","https://openalex.org/W2140405352","https://openalex.org/W2172189177","https://openalex.org/W2549127690","https://openalex.org/W2604942799","https://openalex.org/W2737388338","https://openalex.org/W2788673127","https://openalex.org/W2941809685","https://openalex.org/W2963470929","https://openalex.org/W2976859544","https://openalex.org/W3012816161","https://openalex.org/W3158027551"],"related_works":[],"abstract_inverted_index":{"The":[0],"construction":[1],"of":[2,22,33,45,70,124],"graphical":[3],"representations":[4,18],"is":[5],"crucial":[6],"for":[7,120],"dealing":[8],"with":[9],"high-dimensional":[10],"data":[11,114],"in":[12,76,80],"various":[13],"fields.":[14],"However,":[15],"creating":[16],"such":[17],"often":[19],"encounters":[20],"challenges":[21],"conventional":[23],"methods.":[24],"In":[25],"this":[26],"work,":[27],"we":[28,41],"present":[29],"a":[30,34,77,118],"novel":[31,111],"methodology":[32],"Robust":[35],"Simplex":[36],"Sparse":[37],"Representation":[38],"(RSSR).":[39],"Additionally,":[40],"address":[42],"the":[43,46,57,71],"optimization":[44],"standard":[47],"simplex":[48],"by":[49],"introducing":[50],"an":[51],"effective":[52],"iterative":[53],"algorithm":[54,64],"known":[55],"as":[56],"Proximal":[58],"Alternating":[59],"Linearized":[60],"Minimization":[61],"method.":[62],"Our":[63,108],"achieves":[65],"global":[66],"convergence":[67,69],"and":[68,74,88,95,102,116],"objective":[72],"function":[73],"results":[75],"notable":[78],"reduction":[79],"computational":[81],"expenses":[82],"while":[83],"bolstering":[84],"resilience":[85],"to":[86],"outliers":[87],"noise.":[89],"Extensive":[90],"experiments":[91],"on":[92],"both":[93],"synthetic":[94],"real-world":[96],"datasets":[97],"validate":[98],"our":[99],"method\u2019s":[100],"effectiveness":[101],"advantages":[103],"over":[104],"state-of-the-art":[105],"clustering":[106],"techniques.":[107],"research":[109],"offers":[110],"insights":[112],"into":[113],"representation":[115],"establishes":[117],"foundation":[119],"more":[121],"precise":[122],"analyses":[123],"big":[125],"data.":[126]},"counts_by_year":[],"updated_date":"2026-04-09T06:08:40.794217","created_date":"2026-04-08T00:00:00"}