{"id":"https://openalex.org/W2735394685","doi":"https://doi.org/10.1109/ijcnn.2017.7966413","title":"Robust greedy deep dictionary learning for ECG arrhythmia classification","display_name":"Robust greedy deep dictionary learning for ECG arrhythmia classification","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2735394685","doi":"https://doi.org/10.1109/ijcnn.2017.7966413","mag":"2735394685"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7966413","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966413","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","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/A5020310463","display_name":"Angshul Majumdar","orcid":"https://orcid.org/0000-0002-1065-3000"},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]},{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Angshul Majumdar","raw_affiliation_strings":["IIIT Delhi, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"IIIT Delhi, New Delhi, India","institution_ids":["https://openalex.org/I119939252","https://openalex.org/I68891433"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028143781","display_name":"Rabab Ward","orcid":"https://orcid.org/0000-0002-2471-1902"},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Rabab Ward","raw_affiliation_strings":["University of British Columbia, Vancouver, Canada"],"affiliations":[{"raw_affiliation_string":"University of British Columbia, Vancouver, Canada","institution_ids":["https://openalex.org/I141945490"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5020310463"],"corresponding_institution_ids":["https://openalex.org/I119939252","https://openalex.org/I68891433"],"apc_list":null,"apc_paid":null,"fwci":4.0291,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.95167431,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4400","last_page":"4407"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9958999752998352,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9894999861717224,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7396237850189209},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.726183295249939},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6874943375587463},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6630210876464844},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.6623337268829346},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6526232957839966},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5168371796607971},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5035344958305359},{"id":"https://openalex.org/keywords/dictionary-learning","display_name":"Dictionary learning","score":0.5029069781303406},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46503323316574097},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4237218499183655},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.40032505989074707}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7396237850189209},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.726183295249939},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6874943375587463},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6630210876464844},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.6623337268829346},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6526232957839966},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5168371796607971},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5035344958305359},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.5029069781303406},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46503323316574097},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4237218499183655},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.40032505989074707},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2017.7966413","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966413","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332753","display_name":"Qatar National Research Fund","ror":"https://ror.org/01svaqq28"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W189596042","https://openalex.org/W1522345735","https://openalex.org/W1902027874","https://openalex.org/W1994670735","https://openalex.org/W2025508891","https://openalex.org/W2042092375","https://openalex.org/W2042211079","https://openalex.org/W2044170013","https://openalex.org/W2046033161","https://openalex.org/W2054912984","https://openalex.org/W2063923412","https://openalex.org/W2068861059","https://openalex.org/W2086847993","https://openalex.org/W2099866409","https://openalex.org/W2108396267","https://openalex.org/W2108665656","https://openalex.org/W2111833294","https://openalex.org/W2115706991","https://openalex.org/W2115959656","https://openalex.org/W2119605622","https://openalex.org/W2124386111","https://openalex.org/W2128741343","https://openalex.org/W2135848147","https://openalex.org/W2136437631","https://openalex.org/W2146842127","https://openalex.org/W2151205988","https://openalex.org/W2158006878","https://openalex.org/W2160547390","https://openalex.org/W2163398148","https://openalex.org/W2164122462","https://openalex.org/W2218318129","https://openalex.org/W2289846183","https://openalex.org/W2405123320","https://openalex.org/W2560042709","https://openalex.org/W2565813212","https://openalex.org/W2589565096","https://openalex.org/W2594656819","https://openalex.org/W2751807933","https://openalex.org/W4231109964","https://openalex.org/W4293718213","https://openalex.org/W6607775107","https://openalex.org/W6656594098","https://openalex.org/W6680418607","https://openalex.org/W6688386640","https://openalex.org/W6764512485"],"related_works":["https://openalex.org/W4394785709","https://openalex.org/W4296978181","https://openalex.org/W2912987408","https://openalex.org/W3004801820","https://openalex.org/W4281672036","https://openalex.org/W4313444753","https://openalex.org/W4230582276","https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W2937381246"],"abstract_inverted_index":{"This":[0],"work":[1],"proposes":[2],"a":[3,81,127,133],"new":[4],"deep":[5,12,150,160,170,221],"learning":[6,14,20,151,171,194,222],"method":[7,182,190,225],"which":[8],"we":[9,79,143],"call":[10],"robust":[11,82],"dictionary":[13,77],"RDDL.":[15],"RDDL":[16,54],"is":[17,73,102,124,191],"suitable":[18],"for":[19,61,193,202],"representations":[21,195],"from":[22,111],"signals":[23,49],"corrupted":[24],"with":[25,147,214],"sparse":[26,94],"but":[27],"large":[28],"outliers":[29,44],"such":[30],"as":[31,217,219],"artifacts":[32],"and":[33,52,158,176,201],"noise":[34],"that":[35,72,114],"are":[36,45,119,165],"more":[37],"heavy":[38],"tailed":[39],"than":[40],"Gaussian":[41],"distributions.":[42],"Such":[43],"common":[46],"in":[47,76,126],"biomedical":[48],"e.g.":[50],"EEG":[51],"ECG.":[53],"learns":[55],"multiple":[56,115],"levels":[57,116],"of":[58,66,117,139,141,196],"non-linear":[59],"dictionaries":[60,118],"representing":[62],"the":[63,67,105,112,137,154,159,184,188,206,227],"data.":[64],"Instead":[65],"standard":[68],"Euclidean":[69],"cost":[70,88],"function":[71],"usually":[74],"employed":[75],"learning,":[78],"propose":[80],"l":[83,97],"<sub":[84,98],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[85,99],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[86,100],"-norm":[87,101],"function.":[89],"In":[90,178],"order":[91],"to":[92],"achieve":[93],"representation,":[95],"an":[96],"imposed":[103],"on":[104,168,220],"learned":[106],"representation.":[107],"The":[108,121],"`depth'":[109],"arises":[110],"fact":[113],"learnt.":[120],"full":[122],"formulation":[123],"solved":[125],"greedy":[128],"fashion,":[129],"one":[130],"layer":[131],"at":[132],"time.":[134],"To":[135],"study":[136],"extent":[138],"usefulness":[140],"RDDL,":[142],"first":[144],"benchmark":[145,169],"it":[146,213],"two":[148],"wellknown":[149],"tools":[152],"-":[153,173],"stacked":[155],"denoising":[156],"autoencoder":[157],"belief":[161],"network":[162],"methods;":[163],"experiments":[164],"carried":[166],"out":[167],"datasets":[172],"MNIST,":[174],"CIFAR-10":[175],"SVHN.":[177],"all":[179],"cases,":[180],"our":[181],"yields":[183,226],"best":[185,228],"results.":[186,229],"Then":[187],"proposed":[189],"used":[192],"ECG":[197],"data":[198],"(containing":[199],"arficacts)":[200],"their":[203],"classification":[204,209],"using":[205],"MIT-BIH":[207],"arrhythmia":[208],"database.":[210],"We":[211],"compare":[212],"traditional":[215],"techniques":[216],"well":[218],"tools.":[223],"Our":[224]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}