{"id":"https://openalex.org/W2588580177","doi":"https://doi.org/10.1109/ssci.2016.7849952","title":"Mining complex hyperspectral ALMA cubes for structure with neural machine learning","display_name":"Mining complex hyperspectral ALMA cubes for structure with neural machine learning","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2588580177","doi":"https://doi.org/10.1109/ssci.2016.7849952","mag":"2588580177"},"language":"en","primary_location":{"id":"doi:10.1109/ssci.2016.7849952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2016.7849952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","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/A5009569707","display_name":"Erzs\u00e9bet Mer\u00e9nyi","orcid":"https://orcid.org/0000-0001-8705-6186"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Erzsebet Merenyi","raw_affiliation_strings":["Department of Statistics, Rice University, Houston, Texas"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Rice University, Houston, Texas","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027174409","display_name":"Joshua P. Taylor","orcid":"https://orcid.org/0000-0002-8512-6926"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joshua Taylor","raw_affiliation_strings":["Department of Statistics, Rice University, Houston, Texas"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Rice University, Houston, Texas","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060696968","display_name":"Andrea Isella","orcid":"https://orcid.org/0000-0001-8061-2207"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrea Isella","raw_affiliation_strings":["Department of Physics and Astronomy, Rice University, Houston, Texas"],"affiliations":[{"raw_affiliation_string":"Department of Physics and Astronomy, Rice University, Houston, Texas","institution_ids":["https://openalex.org/I74775410"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5009569707"],"corresponding_institution_ids":["https://openalex.org/I74775410"],"apc_list":null,"apc_paid":null,"fwci":1.2918,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.85812712,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9843000173568726,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9843000173568726,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11111","display_name":"Spectroscopy and Laser Applications","score":0.9277999997138977,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8346894979476929},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6453163623809814},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5433509945869446},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5325782299041748},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42763689160346985},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3476813733577728}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8346894979476929},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6453163623809814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5433509945869446},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5325782299041748},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42763689160346985},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3476813733577728}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssci.2016.7849952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2016.7849952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W229097380","https://openalex.org/W1537525636","https://openalex.org/W1537699624","https://openalex.org/W1659336119","https://openalex.org/W1761871661","https://openalex.org/W1965508489","https://openalex.org/W1991848143","https://openalex.org/W2015953751","https://openalex.org/W2016381774","https://openalex.org/W2024338444","https://openalex.org/W2042169845","https://openalex.org/W2047940964","https://openalex.org/W2053776925","https://openalex.org/W2088704657","https://openalex.org/W2107402227","https://openalex.org/W2119019914","https://openalex.org/W2127048411","https://openalex.org/W2131681506","https://openalex.org/W2139370468","https://openalex.org/W2142363274","https://openalex.org/W2151616049","https://openalex.org/W2164998314","https://openalex.org/W2168487341","https://openalex.org/W2368663335","https://openalex.org/W2409088016","https://openalex.org/W3023044562","https://openalex.org/W3036512766","https://openalex.org/W3099768174","https://openalex.org/W3102641634","https://openalex.org/W3104368567","https://openalex.org/W3105041955","https://openalex.org/W4293374332","https://openalex.org/W6608852248","https://openalex.org/W6636973607","https://openalex.org/W6638004812"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W2033914206"],"abstract_inverted_index":{"Astronomy":[0],"is":[1,160],"producing":[2],"the":[3,11,18,26,33,49,54,65,98,107,115,139,208,214,218],"largest":[4],"\u201cBig":[5],"Data\u201d":[6],"sets":[7,254],"today":[8],"and":[9,22,32,44,51,76,104,106,137,159,221],"in":[10,60,82,90,142,173,257],"near":[12],"future,":[13],"with":[14,85,234],"instruments":[15],"such":[16,120,191],"as":[17,121],"Atacama":[19],"Large":[20,27],"Millimeter":[21],"sub-millimeter":[23],"Array":[24,36],"(ALMA),":[25],"Synoptic":[28],"Survey":[29],"Telescope":[30],"(LSST),":[31],"Square":[34],"Kilometer":[35],"(SKA).":[37],"These":[38],"observations":[39],"afford":[40],"a":[41,91,147],"deeper,":[42],"wider,":[43],"more":[45],"dynamic":[46],"glimpse":[47],"into":[48,187],"structure":[50,117,201],"composition":[52],"of":[53,94,114,118,162,165,207,229,244,251],"universe":[55],"than":[56],"ever":[57],"before.":[58],"However,":[59],"addition":[61],"to":[62,240],"unprecedented":[63,69],"volume,":[64],"data":[66,176,253],"also":[67],"exhibit":[68],"complexity,":[70],"mandating":[71],"new":[72],"approaches":[73],"for":[74,216,248],"extracting":[75],"summarizing":[77],"relevant":[78],"information.":[79],"ALMA":[80,205],"data,":[81],"particular,":[83],"challenges":[84],"very":[86],"high":[87,108],"dimensionality":[88],"(measurements":[89],"large":[92,252],"number":[93],"spectral":[95,109,156],"channels)":[96],"where":[97],"dimensions":[99],"represent":[100],"both":[101,217],"compositional":[102],"information":[103,141],"velocities,":[105],"resolution":[110],"allows":[111],"detailed":[112],"interpretation":[113],"kinematic":[116],"sources":[119],"molecular":[122],"clouds":[123],"or":[124,256],"protoplanetary":[125,209],"disks.":[126],"Traditional":[127],"tools":[128],"like":[129],"moment":[130],"maps":[131],"can":[132,153,238],"no":[133],"longer":[134],"fully":[135],"exploit":[136],"visualize":[138],"rich":[140],"these":[143,174],"data.":[144,192],"We":[145,193,212],"present":[146],"neural":[148],"map-based":[149],"clustering":[150,179,246],"approach":[151],"that":[152],"utilize":[154],"all":[155],"channels":[157],"simultaneously":[158],"capable":[161],"finding":[163],"clusters":[164],"widely":[166],"varying":[167],"statistical":[168],"properties,":[169],"which":[170,237],"are":[171],"expected":[172],"complex":[175],"sets.":[177],"Many":[178],"methods,":[180],"including":[181],"modern":[182],"graph":[183,231],"segmentation":[184,232],"algorithms,":[185],"run":[186],"limitations":[188],"when":[189],"encountering":[190],"demonstrate":[194],"our":[195,245],"tools,":[196],"collectively":[197],"named":[198],"\u201cNeuroScope\u201d,":[199],"through":[200],"mining":[202],"from":[203],"an":[204,227],"image":[206],"disk":[210],"HD142527.":[211],"highlight":[213],"advantages":[215],"emerging":[219],"details":[220],"visualization.":[222],"In":[223],"addition,":[224],"we":[225],"explore":[226],"augmentation":[228],"leading":[230],"algorithms":[233],"NeuroScope":[235],"products,":[236],"lead":[239],"efficient":[241],"full":[242],"automation":[243],"process":[247],"fast":[249],"distillation":[250],"on-board":[255],"archives.":[258]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
