{"id":"https://openalex.org/W2588266755","doi":"https://doi.org/10.1109/mfi.2016.7849471","title":"Learning representations for discrete sensor networks using tensor decompositions","display_name":"Learning representations for discrete sensor networks using tensor decompositions","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2588266755","doi":"https://doi.org/10.1109/mfi.2016.7849471","mag":"2588266755"},"language":"en","primary_location":{"id":"doi:10.1109/mfi.2016.7849471","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi.2016.7849471","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","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/A5008642173","display_name":"Stephan Baier","orcid":"https://orcid.org/0000-0001-9159-3152"},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Stephan Baier","raw_affiliation_strings":["Ludwig Maximilian University of Munich, Munich"],"affiliations":[{"raw_affiliation_string":"Ludwig Maximilian University of Munich, Munich","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055134806","display_name":"Denis Krompa\u00df","orcid":null},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Denis Krompass","raw_affiliation_strings":["Siemens AG, Munich"],"affiliations":[{"raw_affiliation_string":"Siemens AG, Munich","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074808403","display_name":"Volker Tresp","orcid":"https://orcid.org/0000-0001-9428-3686"},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]},{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Volker Tresp","raw_affiliation_strings":["Siemens AG, Ludwig Maximilian University of Munich, Munich"],"affiliations":[{"raw_affiliation_string":"Siemens AG, Ludwig Maximilian University of Munich, Munich","institution_ids":["https://openalex.org/I1325886976","https://openalex.org/I8204097"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5008642173"],"corresponding_institution_ids":["https://openalex.org/I8204097"],"apc_list":null,"apc_paid":null,"fwci":0.1476,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.45701357,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"16","issue":null,"first_page":"84","last_page":"89"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9593999981880188,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9420999884605408,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/tensor","display_name":"Tensor (intrinsic definition)","score":0.7308614253997803},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6816741824150085},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6198374032974243},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"Tensor decomposition","score":0.6061383485794067},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5940998792648315},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5655148029327393},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4813128709793091},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4359634220600128},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.34538015723228455},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32741016149520874},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2676769495010376},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17386600375175476}],"concepts":[{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.7308614253997803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6816741824150085},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6198374032974243},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.6061383485794067},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5940998792648315},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5655148029327393},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4813128709793091},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4359634220600128},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34538015723228455},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32741016149520874},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2676769495010376},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17386600375175476},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/mfi.2016.7849471","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi.2016.7849471","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","raw_type":"proceedings-article"},{"id":"mag:2749674943","is_oa":false,"landing_page_url":"http://jglobal.jst.go.jp/en/public/201702240528593185","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","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":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W205829674","https://openalex.org/W1614298861","https://openalex.org/W1636040588","https://openalex.org/W1798945469","https://openalex.org/W1884516485","https://openalex.org/W1963826206","https://openalex.org/W1982524534","https://openalex.org/W1993482030","https://openalex.org/W2016753842","https://openalex.org/W2024165284","https://openalex.org/W2054141820","https://openalex.org/W2063125621","https://openalex.org/W2088522763","https://openalex.org/W2097286355","https://openalex.org/W2101234009","https://openalex.org/W2121739212","https://openalex.org/W2128569883","https://openalex.org/W2131524184","https://openalex.org/W2133564696","https://openalex.org/W2145287260","https://openalex.org/W2170239483","https://openalex.org/W2226877337","https://openalex.org/W2294329458","https://openalex.org/W2384495648","https://openalex.org/W2950577311","https://openalex.org/W2962954346","https://openalex.org/W2963048316","https://openalex.org/W2964308564","https://openalex.org/W3100857292","https://openalex.org/W3120740533","https://openalex.org/W4293478541","https://openalex.org/W4295547509","https://openalex.org/W6608344535","https://openalex.org/W6636505171","https://openalex.org/W6636510571","https://openalex.org/W6638060716","https://openalex.org/W6639281467","https://openalex.org/W6674949343","https://openalex.org/W6675354045","https://openalex.org/W6679434410","https://openalex.org/W6679667936","https://openalex.org/W6689011240"],"related_works":["https://openalex.org/W4379256054","https://openalex.org/W2093953080","https://openalex.org/W2911706637","https://openalex.org/W47805180","https://openalex.org/W2963838862","https://openalex.org/W3015641590","https://openalex.org/W3216281372","https://openalex.org/W2987657992","https://openalex.org/W2949531434","https://openalex.org/W4286927328"],"abstract_inverted_index":{"With":[0],"the":[1,36,39,53,63,70,82,127,130,134,164],"rising":[2],"number":[3],"of":[4,38,51,65,94,105,129],"sensing":[5],"devices":[6],"installed":[7],"in":[8,133,141],"today's":[9],"and":[10,29,77,97,111,166,177],"future":[11],"sensor":[12,41,75],"networks,":[13],"there":[14],"is":[15],"an":[16],"increasing":[17],"demand":[18],"for":[19,102,120,172],"machine":[20,43,181],"learning":[21,44,96,182],"solutions":[22],"performing":[23],"tasks":[24,176],"like":[25],"automatic":[26],"behavior":[27],"detection":[28],"decision":[30],"making.":[31],"In":[32,58,155],"particular,":[33],"to":[34,68,152],"classify":[35],"state":[37],"complete":[40],"network,":[42],"models":[45,67],"are":[46,49,137,149],"needed,":[47],"which":[48],"capable":[50],"fusing":[52],"information":[54],"from":[55],"multiple":[56,73],"sensors.":[57],"this":[59],"paper":[60],"we":[61,161],"examine":[62],"use":[64],"tensor":[66,124],"describe":[69],"relationship":[71],"between":[72],"discrete":[74,173],"outputs":[76],"attendant":[78],"class":[79],"labels":[80],"describing":[81],"overall":[83],"system":[84],"state.":[85],"Tensor":[86,167],"decompositions":[87,169],"can":[88],"be":[89],"considered":[90],"as":[91],"a":[92,103,117],"form":[93],"representation":[95],"they":[98],"have":[99],"been":[100],"used":[101,132],"variety":[104],"tasks,":[106,143],"e.g.":[107],"knowledge":[108],"graph":[109],"modeling":[110],"EEG":[112],"data":[113],"analysis.":[114],"We":[115],"propose":[116],"new":[118],"approach":[119],"multiclass":[121],"classification":[122,136],"using":[123],"decompositions.":[125],"As":[126],"dimensions":[128],"tensors":[131],"multi-sensor":[135,174],"much":[138],"higher":[139],"than":[140],"traditional":[142],"not":[144],"all":[145],"standard":[146],"decomposition":[147],"approaches":[148],"applicable":[150],"due":[151],"scaling":[153],"problems.":[154],"our":[156],"experiments":[157],"on":[158],"real":[159],"data,":[160],"show":[162],"that":[163],"PARAFAC":[165],"Train":[168],"work":[170],"well":[171],"fusion":[175],"outperform":[178],"other":[179],"state-of-the-art":[180],"algorithms.":[183]},"counts_by_year":[{"year":2017,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
