{"id":"https://openalex.org/W4312367196","doi":"https://doi.org/10.1109/icpr56361.2022.9956663","title":"Surprising Effectiveness of Random Feature Embeddings in eXtreme Classification","display_name":"Surprising Effectiveness of Random Feature Embeddings in eXtreme Classification","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4312367196","doi":"https://doi.org/10.1109/icpr56361.2022.9956663"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956663","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956663","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th 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/A5041041585","display_name":"Yashaswi Verma","orcid":"https://orcid.org/0000-0003-2317-2641"},"institutions":[{"id":"https://openalex.org/I154549908","display_name":"Indian Institute of Technology Jodhpur","ror":"https://ror.org/03yacj906","country_code":"IN","type":"education","lineage":["https://openalex.org/I154549908"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Yashaswi Verma","raw_affiliation_strings":["IIT Jodhpur,India","IIT Jodhpur, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIT Jodhpur,India","institution_ids":["https://openalex.org/I154549908"]},{"raw_affiliation_string":"IIT Jodhpur, India","institution_ids":["https://openalex.org/I154549908"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5041041585"],"corresponding_institution_ids":["https://openalex.org/I154549908"],"apc_list":null,"apc_paid":null,"fwci":0.1039,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.32398622,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1836","last_page":"1842"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9995999932289124,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9995999932289124,"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/T11309","display_name":"Music and Audio Processing","score":0.991599977016449,"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"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9901000261306763,"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/computer-science","display_name":"Computer science","score":0.8314135074615479},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6484518051147461},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6038944125175476},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.5719914436340332},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5346162915229797},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5319222211837769},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.505497932434082},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4934362471103668},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4701077342033386},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.46431657671928406},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3493915796279907},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.08521202206611633}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8314135074615479},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6484518051147461},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6038944125175476},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.5719914436340332},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5346162915229797},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5319222211837769},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.505497932434082},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4934362471103668},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4701077342033386},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.46431657671928406},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3493915796279907},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.08521202206611633},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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},{"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/icpr56361.2022.9956663","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956663","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":46,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1505090736","https://openalex.org/W1531259569","https://openalex.org/W1834987204","https://openalex.org/W1940008012","https://openalex.org/W2068074736","https://openalex.org/W2150385485","https://openalex.org/W2156336347","https://openalex.org/W2169446650","https://openalex.org/W2183087644","https://openalex.org/W2359108789","https://openalex.org/W2362855512","https://openalex.org/W2437817353","https://openalex.org/W2461743311","https://openalex.org/W2520348554","https://openalex.org/W2607041163","https://openalex.org/W2739996966","https://openalex.org/W2743021690","https://openalex.org/W2744136723","https://openalex.org/W2788125153","https://openalex.org/W2886305600","https://openalex.org/W2896457183","https://openalex.org/W2906963924","https://openalex.org/W2921113176","https://openalex.org/W2962717353","https://openalex.org/W2970597249","https://openalex.org/W2979473749","https://openalex.org/W2987098737","https://openalex.org/W2995855391","https://openalex.org/W3037422790","https://openalex.org/W3039729104","https://openalex.org/W3080802002","https://openalex.org/W3114079967","https://openalex.org/W6632006871","https://openalex.org/W6640485552","https://openalex.org/W6681903532","https://openalex.org/W6685191470","https://openalex.org/W6685974025","https://openalex.org/W6718172739","https://openalex.org/W6718527420","https://openalex.org/W6753458378","https://openalex.org/W6755207826","https://openalex.org/W6763701032","https://openalex.org/W6767075311","https://openalex.org/W6772092003","https://openalex.org/W6780300825"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W2049082574"],"abstract_inverted_index":{"The":[0],"goal":[1],"of":[2,20,27,93,105,157],"eXtreme":[3],"Multi-label":[4],"Learning":[5],"(XML)":[6],"is":[7,63,91],"to":[8,39,64,112,167],"automatically":[9],"annotate":[10],"a":[11,30,68,77],"given":[12],"data":[13],"point":[14],"with":[15,56,119,143],"the":[16,136,168,172],"most":[17],"relevant":[18],"subset":[19],"labels":[21,28],"from":[22],"an":[23,57,103],"extremely":[24],"large":[25],"vocabulary":[26],"(e.g.,":[29],"million":[31],"labels).":[32],"Lately,":[33],"many":[34,144],"attempts":[35],"have":[36,177],"been":[37],"made":[38],"address":[40],"this":[41,51,72],"problem":[42],"that":[43,135],"achieve":[44,113],"reasonable":[45],"performance":[46],"on":[47,129,171],"benchmark":[48],"datasets.":[49],"In":[50],"paper,":[52],"rather":[53],"than":[54],"coming-up":[55],"altogether":[58],"new":[59],"method,":[60],"our":[61,180],"objective":[62],"present":[65],"and":[66,79,96,125,160],"validate":[67],"simple":[69],"baseline":[70],"for":[71,182],"task.":[73],"Precisely,":[74],"we":[75,100],"investigate":[76],"global":[78],"structure":[80],"preserving":[81],"feature":[82],"embedding":[83],"technique":[84],"using":[85],"random":[86],"projections":[87],"whose":[88],"learning":[89],"phase":[90],"independent":[92],"training":[94,124,158],"samples":[95],"label":[97],"vocabulary.":[98],"Further,":[99],"show":[101,134],"how":[102],"ensemble":[104],"multiple":[106],"such":[107],"learners":[108],"can":[109],"be":[110],"used":[111],"further":[114],"boost":[115],"in":[116,123,155,164],"prediction":[117,126],"accuracy":[118,141],"only":[120],"linear":[121],"increase":[122],"time.":[127],"Experiments":[128],"three":[130],"public":[131,174],"XML":[132],"benchmarks":[133],"proposed":[137],"approach":[138],"obtains":[139],"competitive":[140],"compared":[142,166],"existing":[145],"methods.":[146],"Additionally,":[147],"it":[148],"also":[149,178],"provides":[150],"around":[151,161],"6572\u00d7":[152],"speed-up":[153],"ratio":[154],"terms":[156],"time":[159],"14.7\u00d7":[162],"reduction":[163],"model-size":[165],"closest":[169],"competitors":[170],"largest":[173],"dataset.":[175],"We":[176],"shared":[179],"code":[181],"reproducibility.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}
