{"id":"https://openalex.org/W2321101928","doi":"https://doi.org/10.1109/aipr.2015.7444552","title":"Automated variability selection in time-domain imaging surveys using sparse representations with learned dictionaries","display_name":"Automated variability selection in time-domain imaging surveys using sparse representations with learned dictionaries","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2321101928","doi":"https://doi.org/10.1109/aipr.2015.7444552","mag":"2321101928"},"language":"en","primary_location":{"id":"doi:10.1109/aipr.2015.7444552","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2015.7444552","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","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/A5051086336","display_name":"Daniela I. Moody","orcid":"https://orcid.org/0000-0002-4452-8208"},"institutions":[{"id":"https://openalex.org/I4210164897","display_name":"Descartes Labs (United States)","ror":"https://ror.org/05a9ar681","country_code":"US","type":"company","lineage":["https://openalex.org/I4210164897"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Daniela I. Moody","raw_affiliation_strings":["Descartes Labs, Los Alamos, NM"],"affiliations":[{"raw_affiliation_string":"Descartes Labs, Los Alamos, NM","institution_ids":["https://openalex.org/I4210164897"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087387810","display_name":"P. R. Wo\u017aniak","orcid":"https://orcid.org/0000-0002-9919-3310"},"institutions":[{"id":"https://openalex.org/I1343871089","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I1343871089","https://openalex.org/I198811213","https://openalex.org/I4210120050"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Przemek R. Wozniak","raw_affiliation_strings":["Los Alamos National Laboratory, Los Alamos, NM"],"affiliations":[{"raw_affiliation_string":"Los Alamos National Laboratory, Los Alamos, NM","institution_ids":["https://openalex.org/I1343871089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038579760","display_name":"Steven P. Brumby","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164897","display_name":"Descartes Labs (United States)","ror":"https://ror.org/05a9ar681","country_code":"US","type":"company","lineage":["https://openalex.org/I4210164897"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Steven P. Brumby","raw_affiliation_strings":["Descartes Labs, Los Alamos, NM"],"affiliations":[{"raw_affiliation_string":"Descartes Labs, Los Alamos, NM","institution_ids":["https://openalex.org/I4210164897"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5051086336"],"corresponding_institution_ids":["https://openalex.org/I4210164897"],"apc_list":null,"apc_paid":null,"fwci":0.39628981,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.65930379,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"54","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9898999929428101,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9879000186920166,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6987967491149902},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6562293767929077},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5069103837013245},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5062565207481384},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.43144312500953674},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3813655972480774},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36116474866867065},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15788742899894714}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6987967491149902},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6562293767929077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5069103837013245},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5062565207481384},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.43144312500953674},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3813655972480774},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36116474866867065},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15788742899894714},{"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:10.1109/aipr.2015.7444552","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2015.7444552","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","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":24,"referenced_works":["https://openalex.org/W228380312","https://openalex.org/W1666193446","https://openalex.org/W2056683140","https://openalex.org/W2078204800","https://openalex.org/W2082855665","https://openalex.org/W2083217178","https://openalex.org/W2115429828","https://openalex.org/W2115755118","https://openalex.org/W2129812935","https://openalex.org/W2145889472","https://openalex.org/W2149846926","https://openalex.org/W2150409012","https://openalex.org/W2151693816","https://openalex.org/W2154473820","https://openalex.org/W2156598558","https://openalex.org/W2160547390","https://openalex.org/W2184852195","https://openalex.org/W2548954850","https://openalex.org/W2911964244","https://openalex.org/W3104062568","https://openalex.org/W3169271739","https://openalex.org/W4235713725","https://openalex.org/W6686418764","https://openalex.org/W6785671740"],"related_works":["https://openalex.org/W4205762803","https://openalex.org/W2535856026","https://openalex.org/W2265065644","https://openalex.org/W2130187411","https://openalex.org/W2134699697","https://openalex.org/W3017188156","https://openalex.org/W2322875716","https://openalex.org/W2383516975","https://openalex.org/W2387058352","https://openalex.org/W1964749215"],"abstract_inverted_index":{"Exponential":[0],"growth":[1],"in":[2,86],"data":[3,103,154,207],"streams":[4],"and":[5,27,65,89,112,217],"discovery":[6],"power":[7],"delivered":[8],"by":[9,43,62,83,223,258],"modern":[10],"time-domain":[11],"imaging":[12],"surveys":[13],"creates":[14],"a":[15,55,76,136,160,236,242],"pressing":[16],"need":[17],"for":[18,47,150,184],"variability":[19],"extraction":[20],"algorithms":[21],"that":[22,46],"are":[23,41,156,253],"both":[24],"fully":[25],"automated":[26],"highly":[28],"reliable.":[29],"The":[30,101,153],"current":[31],"state":[32],"of":[33,58,114,127,163,202,210,247,262],"the":[34,44,52,91,107,140,167,197,260],"art":[35],"methods":[36],"based":[37,195],"on":[38,196],"image":[39,71],"differencing":[40],"limited":[42],"fact":[45],"every":[48],"real":[49],"variable":[50],"source":[51],"algorithm":[53,200],"returns":[54],"large":[56],"number":[57,162],"bogus":[59],"\"detections\"":[60],"caused":[61],"atmospheric":[63],"effects":[64],"instrumental":[66],"signatures":[67],"coupled":[68],"with":[69,121,159,205,235],"imperfect":[70],"processing.":[72],"Here":[73],"we":[74],"present":[75],"new":[77,95],"approach":[78],"to":[79,93,180,212,255,269],"this":[80],"problem":[81],"inspired":[82],"recent":[84],"advances":[85],"computer":[87,214],"vision":[88,215],"train":[90],"machine":[92,187],"learn":[94],"features":[96,183,216,221,228,252],"directly":[97],"from":[98,106,267],"pixel":[99],"data.":[100],"training":[102],"set":[104,126,147],"comes":[105],"Palomar":[108],"Transient":[109],"Factory":[110],"survey":[111],"consists":[113],"small":[115,161],"images":[116],"centered":[117],"around":[118],"transient":[119],"candidates":[120],"known":[122],"real/bogus":[123],"classification.":[124],"This":[125],"high-dimensional":[128],"vectors":[129,155,208],"(~1000":[130],"features)":[131],"is":[132,170,194,265],"then":[133],"transformed":[134],"into":[135],"linear":[137],"representation":[138,169],"using":[139],"so":[141],"called":[142],"dictionary,":[143],"an":[144,232],"overcomplete":[145],"feature":[146],"constructed":[148,257],"separately":[149],"each":[151],"class.":[152],"well":[157],"approximated":[158],"dictionary":[164,168],"elements,":[165],"i.e.":[166],"sparse.":[171],"We":[172],"show":[173],"how":[174],"sparse":[175],"representations":[176],"can":[177],"be":[178],"used":[179],"construct":[181],"informative":[182],"any":[185],"suitable":[186],"learning":[188],"classifier.":[189],"Our":[190],"top":[191],"level":[192],"classifier":[193],"random":[198],"forest":[199],"(collections":[201],"decision":[203],"trees)":[204],"input":[206],"consisting":[209],"up":[211],"6":[213],"20":[218],"additional":[219],"context":[220],"designed":[222],"subject":[224],"domain":[225],"experts.":[226],"Machine-learned":[227],"alone":[229],"provide":[230],"only":[231],"approximate":[233],"classification":[234],"20%":[237],"missed":[238,263],"detection":[239],"rate":[240,246,261],"at":[241,272],"fixed":[243],"false":[244,274],"positive":[245,275],"1%.":[248],"When":[249],"automatically":[250],"extracted":[251],"appended":[254],"those":[256],"humans,":[259],"detections":[264],"reduced":[266],"8%":[268],"about":[270],"4%":[271],"1%":[273],"rate.":[276]},"counts_by_year":[{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}