{"id":"https://openalex.org/W2975461158","doi":"https://doi.org/10.1109/isit.2019.8849589","title":"Quantizing Signals for Linear Classification","display_name":"Quantizing Signals for Linear Classification","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2975461158","doi":"https://doi.org/10.1109/isit.2019.8849589","mag":"2975461158"},"language":"en","primary_location":{"id":"doi:10.1109/isit.2019.8849589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2019.8849589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Information Theory (ISIT)","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/A5005778563","display_name":"Yahya H. Ezzeldin","orcid":"https://orcid.org/0000-0002-4238-5362"},"institutions":[{"id":"https://openalex.org/I2799798094","display_name":"UCLA Health","ror":"https://ror.org/01d88se56","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2799798094"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yahya H. Ezzeldin","raw_affiliation_strings":["UCLA, Los Angeles, CA, 90095, USA","UCLA, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"UCLA, Los Angeles, CA, 90095, USA","institution_ids":[]},{"raw_affiliation_string":"UCLA, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I2799798094"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056591688","display_name":"Christina Fragouli","orcid":"https://orcid.org/0000-0003-1002-5829"},"institutions":[{"id":"https://openalex.org/I2799798094","display_name":"UCLA Health","ror":"https://ror.org/01d88se56","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2799798094"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christina Fragouli","raw_affiliation_strings":["UCLA, Los Angeles, CA, 90095, USA","UCLA, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"UCLA, Los Angeles, CA, 90095, USA","institution_ids":[]},{"raw_affiliation_string":"UCLA, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I2799798094"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083980887","display_name":"Suhas Diggavi","orcid":"https://orcid.org/0000-0001-7313-9861"},"institutions":[{"id":"https://openalex.org/I2799798094","display_name":"UCLA Health","ror":"https://ror.org/01d88se56","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2799798094"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suhas Diggavi","raw_affiliation_strings":["UCLA, Los Angeles, CA, 90095, USA","UCLA, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"UCLA, Los Angeles, CA, 90095, USA","institution_ids":[]},{"raw_affiliation_string":"UCLA, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I2799798094"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5005778563"],"corresponding_institution_ids":["https://openalex.org/I2799798094"],"apc_list":null,"apc_paid":null,"fwci":0.5559,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.7074718,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"912","last_page":"916"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9926000237464905,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.544080913066864},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.36661434173583984},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3655601739883423},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.345045804977417}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.544080913066864},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36661434173583984},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3655601739883423},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.345045804977417}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit.2019.8849589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2019.8849589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Information Theory (ISIT)","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":12,"referenced_works":["https://openalex.org/W1977862970","https://openalex.org/W2109053700","https://openalex.org/W2112905930","https://openalex.org/W2124317079","https://openalex.org/W2154546286","https://openalex.org/W2163382065","https://openalex.org/W2169866873","https://openalex.org/W2753338564","https://openalex.org/W2767580541","https://openalex.org/W3215641518","https://openalex.org/W6744501015","https://openalex.org/W6748089513"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"In":[0,30],"many":[1],"machine":[2],"learning":[3],"applications,":[4,62],"once":[5],"we":[6,16,33,96],"have":[7],"learned":[8],"a":[9,35,49],"classifier,":[10,73],"in":[11],"order":[12],"to":[13,20,43,60,70,79,89],"apply":[14],"it,":[15],"may":[17],"still":[18],"need":[19],"gather":[21],"features":[22],"from":[23],"distributed":[24,77],"sensors":[25],"over":[26,92],"communication":[27],"constrained":[28],"channels.":[29],"this":[31],"paper,":[32],"propose":[34],"polynomial":[36],"complexity":[37],"algorithm":[38],"for":[39],"feature":[40],"quantization":[41,108],"tailored":[42],"minimizing":[44],"the":[45,65,72,103,107],"classification":[46],"error":[47],"of":[48,82],"linear":[50,104],"classifier.":[51],"Our":[52],"scheme":[53],"produces":[54],"scalar":[55],"quantizers":[56],"that":[57],"are":[58],"well-tailored":[59],"delay-sensitive":[61],"operates":[63],"on":[64],"same":[66],"training":[67],"data":[68],"used":[69],"learn":[71],"and":[74,106],"allows":[75],"each":[76,83],"sensor":[78],"operate":[80],"independently":[81],"other.":[84],"Numerical":[85],"evaluation":[86],"indicates":[87],"up":[88],"65%":[90],"benefits":[91],"alternative":[93],"approaches.":[94],"Additionally,":[95],"provide":[97],"an":[98],"example":[99],"where,":[100],"jointly":[101],"designing":[102],"classifier":[105],"scheme,":[109],"can":[110],"outperform":[111],"sequential":[112],"designs.":[113]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-14T06:41:57.775601","created_date":"2025-10-10T00:00:00"}
