{"id":"https://openalex.org/W2609644570","doi":"https://doi.org/10.1109/icpr.2016.7899688","title":"Training robust models using Random Projection","display_name":"Training robust models using Random Projection","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2609644570","doi":"https://doi.org/10.1109/icpr.2016.7899688","mag":"2609644570"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2016.7899688","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899688","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","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/A5000403961","display_name":"Nguy\u1ec5n Xu\u00e2n Vinh","orcid":"https://orcid.org/0000-0002-7275-750X"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Nguyen Xuan Vinh","raw_affiliation_strings":["Department of Computing and Information Systems, The University of Melbourne, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Computing and Information Systems, The University of Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070030398","display_name":"Sarah Erfani","orcid":"https://orcid.org/0000-0003-0885-0643"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sarah Erfani","raw_affiliation_strings":["Department of Computing and Information Systems, The University of Melbourne, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Computing and Information Systems, The University of Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083707053","display_name":"Sakrapee Paisitkriangkrai","orcid":null},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sakrapee Paisitkriangkrai","raw_affiliation_strings":["Department of Computing and Information Systems, The University of Melbourne, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Computing and Information Systems, The University of Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101609697","display_name":"James Bailey","orcid":"https://orcid.org/0000-0002-3769-3811"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"James Bailey","raw_affiliation_strings":["Department of Computing and Information Systems, The University of Melbourne, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Computing and Information Systems, The University of Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076014464","display_name":"Christopher Leckie","orcid":"https://orcid.org/0000-0002-4388-0517"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Christopher Leckie","raw_affiliation_strings":["Department of Computing and Information Systems, The University of Melbourne, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Computing and Information Systems, The University of Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069895484","display_name":"Kotagiri Ramamohanarao","orcid":"https://orcid.org/0000-0003-3304-9268"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Kotagiri Ramamohanarao","raw_affiliation_strings":["Department of Computing and Information Systems, The University of Melbourne, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Computing and Information Systems, The University of Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5000403961"],"corresponding_institution_ids":["https://openalex.org/I165779595"],"apc_list":null,"apc_paid":null,"fwci":2.1424,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.91205781,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"531","last_page":"536"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":1.0,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":1.0,"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/T12676","display_name":"Machine Learning and ELM","score":0.9865000247955322,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9843999743461609,"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/random-projection","display_name":"Random projection","score":0.7368932962417603},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7106239795684814},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7019004821777344},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6773750185966492},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6587328910827637},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6384745836257935},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6354259252548218},{"id":"https://openalex.org/keywords/random-noise","display_name":"Random noise","score":0.6138045787811279},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6023039817810059},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5484158396720886},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5240949988365173},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5038432478904724},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.4832197427749634},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4504826068878174},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.43776246905326843},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.30329030752182007}],"concepts":[{"id":"https://openalex.org/C2777036070","wikidata":"https://www.wikidata.org/wiki/Q18393452","display_name":"Random projection","level":2,"score":0.7368932962417603},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7106239795684814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7019004821777344},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6773750185966492},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6587328910827637},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6384745836257935},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6354259252548218},{"id":"https://openalex.org/C2986577269","wikidata":"https://www.wikidata.org/wiki/Q11306265","display_name":"Random noise","level":2,"score":0.6138045787811279},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6023039817810059},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5484158396720886},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5240949988365173},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5038432478904724},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.4832197427749634},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4504826068878174},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.43776246905326843},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30329030752182007},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icpr.2016.7899688","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899688","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:jupiter.its.unimelb.edu.au:11343/282461","is_oa":false,"landing_page_url":"http://hdl.handle.net/11343/282461","pdf_url":null,"source":{"id":"https://openalex.org/S4377196259","display_name":"Minerva Access (University of Melbourne)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I165779595","host_organization_name":"The University of Melbourne","host_organization_lineage":["https://openalex.org/I165779595"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"23rd International Conference on Pattern Recognition (ICPR)","raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W4919037","https://openalex.org/W1521968289","https://openalex.org/W1557504357","https://openalex.org/W1673923490","https://openalex.org/W1678356000","https://openalex.org/W1922655562","https://openalex.org/W1932198206","https://openalex.org/W1945616565","https://openalex.org/W2037757210","https://openalex.org/W2095705004","https://openalex.org/W2100341991","https://openalex.org/W2106525823","https://openalex.org/W2111406701","https://openalex.org/W2111662199","https://openalex.org/W2124136621","https://openalex.org/W2152722485","https://openalex.org/W2163605009","https://openalex.org/W2952825952","https://openalex.org/W2998768810","https://openalex.org/W3120740533","https://openalex.org/W6600213771","https://openalex.org/W6674330103","https://openalex.org/W6682824805","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W4289703016","https://openalex.org/W4310224730","https://openalex.org/W1985505753"],"abstract_inverted_index":{"Regularization":[0],"plays":[1],"an":[2],"important":[3],"role":[4],"in":[5],"machine":[6],"learning":[7],"systems.":[8],"We":[9,20,108,143],"propose":[10],"a":[11,32,116],"novel":[12],"methodology":[13],"for":[14,39],"model":[15,120],"regularization":[16],"using":[17,105],"random":[18,106,112,147],"projection.":[19,107],"demonstrate":[21],"the":[22,70,84,153],"technique":[23],"on":[24],"neural":[25,48,100],"networks,":[26],"since":[27],"such":[28,126,158],"models":[29],"usually":[30],"comprise":[31],"very":[33],"large":[34],"number":[35],"of":[36,55,66,155],"parameters,":[37],"calling":[38],"strong":[40,117],"regularizers.":[41],"It":[42],"has":[43],"been":[44],"shown":[45],"recently":[46],"that":[47,110,146],"networks":[49,101],"are":[50,61,80],"sensitive":[51],"to":[52,123,137,151],"two":[53],"kinds":[54],"samples:":[56],"(i)":[57],"adversarial":[58,138],"samples,":[59,78],"which":[60,79],"generated":[62],"by":[63],"imperceptible":[64],"perturbations":[65],"previously":[67],"correctly-classified":[68],"samples\u2014yet":[69],"network":[71,85],"will":[72,86],"misclassify":[73],"them;":[74],"and":[75,130,140,162],"(ii)":[76],"fooling":[77,141],"completely":[81],"unrecognizable,":[82],"yet":[83],"classify":[87],"them":[88],"with":[89],"extremely":[90],"high":[91],"confidence.":[92],"In":[93],"this":[94],"paper,":[95],"we":[96],"show":[97,109,145],"how":[98],"robust":[99,136],"can":[102],"be":[103],"trained":[104],"while":[111],"projection":[113,148],"acts":[114],"as":[115,127,159],"regularizer,":[118],"boosting":[119],"accuracy":[121],"similar":[122],"other":[124],"regularizers,":[125],"weight":[128],"decay":[129],"dropout,":[131],"it":[132],"is":[133],"far":[134],"more":[135],"noise":[139],"samples.":[142],"further":[144],"also":[149],"helps":[150],"improve":[152],"robustness":[154],"traditional":[156],"classifiers,":[157],"Random":[160],"Forrest":[161],"Gradient":[163],"Boosting":[164],"Machines.":[165]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2017-05-05T00:00:00"}