{"id":"https://openalex.org/W2068612417","doi":"https://doi.org/10.1145/2425333.2425381","title":"Enhanced eigenspace separation transform for classification","display_name":"Enhanced eigenspace separation transform for classification","publication_year":2012,"publication_date":"2012-12-16","ids":{"openalex":"https://openalex.org/W2068612417","doi":"https://doi.org/10.1145/2425333.2425381","mag":"2068612417"},"language":"en","primary_location":{"id":"doi:10.1145/2425333.2425381","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2425333.2425381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing","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/A5072098966","display_name":"K. Ramachandra Murthy","orcid":null},"institutions":[{"id":"https://openalex.org/I6498739","display_name":"Indian Statistical Institute","ror":"https://ror.org/00q2w1j53","country_code":"IN","type":"education","lineage":["https://openalex.org/I6498739"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K. Ramachandra Murthy","raw_affiliation_strings":["Indian Statistical Institute, Kolkata","Indian Statistical institute, Kolkata#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Statistical Institute, Kolkata","institution_ids":["https://openalex.org/I6498739"]},{"raw_affiliation_string":"Indian Statistical institute, Kolkata#TAB#","institution_ids":["https://openalex.org/I6498739"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025656702","display_name":"Ashish Ghosh","orcid":"https://orcid.org/0000-0003-1548-5576"},"institutions":[{"id":"https://openalex.org/I6498739","display_name":"Indian Statistical Institute","ror":"https://ror.org/00q2w1j53","country_code":"IN","type":"education","lineage":["https://openalex.org/I6498739"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ashish Ghosh","raw_affiliation_strings":["Indian Statistical Institute, Kolkata","Indian Statistical institute, Kolkata#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Statistical Institute, Kolkata","institution_ids":["https://openalex.org/I6498739"]},{"raw_affiliation_string":"Indian Statistical institute, Kolkata#TAB#","institution_ids":["https://openalex.org/I6498739"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I6498739"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.13582451,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9991000294685364,"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":0.9991000294685364,"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/T10320","display_name":"Neural Networks and Applications","score":0.9976000189781189,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9966999888420105,"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/orthonormal-basis","display_name":"Orthonormal basis","score":0.5989258289337158},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5893856287002563},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.5732356309890747},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5694741606712341},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5354412198066711},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5290569067001343},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5125021934509277},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4957967698574066},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4881177842617035},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.48115479946136475},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.44732919335365295},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.41184088587760925},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3915059268474579}],"concepts":[{"id":"https://openalex.org/C5806529","wikidata":"https://www.wikidata.org/wiki/Q2365325","display_name":"Orthonormal basis","level":2,"score":0.5989258289337158},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5893856287002563},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.5732356309890747},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5694741606712341},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5354412198066711},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5290569067001343},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5125021934509277},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4957967698574066},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4881177842617035},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.48115479946136475},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.44732919335365295},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.41184088587760925},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3915059268474579},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2425333.2425381","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2425333.2425381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing","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/W1770825568","https://openalex.org/W1983772724","https://openalex.org/W2001141328","https://openalex.org/W2001619934","https://openalex.org/W2021864104","https://openalex.org/W2023155403","https://openalex.org/W2053186076","https://openalex.org/W2075980530","https://openalex.org/W2088900896","https://openalex.org/W2089322632","https://openalex.org/W2096572037","https://openalex.org/W2097308346","https://openalex.org/W2104752854","https://openalex.org/W2105055468","https://openalex.org/W2106053110","https://openalex.org/W2169495281","https://openalex.org/W2990138404","https://openalex.org/W3120740533","https://openalex.org/W4250689175"],"related_works":["https://openalex.org/W1995622179","https://openalex.org/W4391160746","https://openalex.org/W1484111231","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W3089231081","https://openalex.org/W2093956241"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"Enhanced":[3],"Eigenspace":[4,21],"Separation":[5,22],"Transform":[6,23],"(EEST),":[7],"a":[8,57,67,78],"novel":[9],"supervised":[10],"dimensionality":[11,98],"reduction":[12],"technique":[13],"for":[14,117],"classification.":[15],"EEST":[16,28,149],"is":[17,29,80,115,133,161],"motivated":[18],"from":[19],"the":[20,32,35,41,47,53,64,86,90,102,106,121,124,130,146,158,174,177],"(EST).":[24],"The":[25],"criterion":[26],"of":[27,38,56,63,76,89,105,123,129,148,176],"to":[30,51,71,95,119,144,172],"maximize":[31],"difference":[33],"in":[34,40,93],"average":[36,49],"lengths":[37],"vectors":[39],"underlying":[42],"two":[43],"classes,":[44],"and":[45,66,100,157],"minimize":[46],"intra-class":[48],"distances,":[50],"improve":[52,101],"generalization":[54,103],"capacity":[55],"classifier.":[58,107],"We":[59],"propose":[60],"upper":[61,91],"bounds":[62,92],"criterion,":[65],"specific":[68],"solution":[69,79],"space":[70,88],"attain":[72],"these":[73],"bounds.":[74],"Existence":[75],"such":[77],"restricted,":[81],"thereby":[82],"we":[83],"have":[84,141],"considered":[85],"orthonormal":[87],"order":[94],"achieve":[96],"better":[97],"reduction,":[99],"accuracy":[104],"A":[108],"simple":[109],"Nearest":[110],"Neighborhood":[111],"(NN)":[112],"classification":[113,118],"approach":[114],"adopted":[116],"highlight":[120],"novelty":[122],"proposed":[125,131,159,178],"scheme.":[126],"Theoretical":[127],"analysis":[128],"techniques":[132],"also":[134],"carried":[135],"out.":[136],"Different":[137],"synthetic":[138],"data":[139,170],"sets":[140,171],"been":[142],"used":[143],"evaluate":[145],"advantages":[147],"over":[150],"EST.":[151],"Extensive":[152],"empirical":[153],"studies":[154],"are":[155],"made;":[156],"method":[160],"compared":[162],"with":[163],"three":[164],"closely":[165],"related":[166],"schemes":[167],"using":[168],"real-world":[169],"verify":[173],"efficiency":[175],"method.":[179]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}