{"id":"https://openalex.org/W2898557871","doi":"https://doi.org/10.1109/bigdata.2018.8622365","title":"Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large datasets","display_name":"Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large datasets","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2898557871","doi":"https://doi.org/10.1109/bigdata.2018.8622365","mag":"2898557871"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2018.8622365","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622365","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1810.11562","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066361433","display_name":"Henry Kvinge","orcid":"https://orcid.org/0000-0003-4108-1364"},"institutions":[{"id":"https://openalex.org/I92446798","display_name":"Colorado State University","ror":"https://ror.org/03k1gpj17","country_code":"US","type":"education","lineage":["https://openalex.org/I92446798"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Henry Kvinge","raw_affiliation_strings":["Department of Mathematics, Colorado State University, Fort Collins, CO, USA"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Colorado State University, Fort Collins, CO, USA","institution_ids":["https://openalex.org/I92446798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035617478","display_name":"Elin Farnell","orcid":null},"institutions":[{"id":"https://openalex.org/I92446798","display_name":"Colorado State University","ror":"https://ror.org/03k1gpj17","country_code":"US","type":"education","lineage":["https://openalex.org/I92446798"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Elin Farnell","raw_affiliation_strings":["Department of Mathematics, Colorado State University, Fort Collins, CO, USA"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Colorado State University, Fort Collins, CO, USA","institution_ids":["https://openalex.org/I92446798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061567476","display_name":"Michael Kirby","orcid":"https://orcid.org/0000-0001-9802-9263"},"institutions":[{"id":"https://openalex.org/I92446798","display_name":"Colorado State University","ror":"https://ror.org/03k1gpj17","country_code":"US","type":"education","lineage":["https://openalex.org/I92446798"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Kirby","raw_affiliation_strings":["Department of Mathematics, Colorado State University, Fort Collins, CO, USA"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Colorado State University, Fort Collins, CO, USA","institution_ids":["https://openalex.org/I92446798"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078621088","display_name":"Chris Peterson","orcid":"https://orcid.org/0000-0002-3982-6876"},"institutions":[{"id":"https://openalex.org/I92446798","display_name":"Colorado State University","ror":"https://ror.org/03k1gpj17","country_code":"US","type":"education","lineage":["https://openalex.org/I92446798"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chris Peterson","raw_affiliation_strings":["Department of Mathematics, Colorado State University, Fort Collins, CO, USA"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Colorado State University, Fort Collins, CO, USA","institution_ids":["https://openalex.org/I92446798"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5066361433"],"corresponding_institution_ids":["https://openalex.org/I92446798"],"apc_list":null,"apc_paid":null,"fwci":0.208,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5285012,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"41","issue":null,"first_page":"1045","last_page":"1051"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9937999844551086,"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":0.9937999844551086,"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/T10057","display_name":"Face and Expression Recognition","score":0.9824000000953674,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9805999994277954,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.7245786190032959},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.566650927066803},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5658774971961975},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5581303834915161},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5132222175598145},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5088089108467102},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5075434446334839},{"id":"https://openalex.org/keywords/tuple","display_name":"Tuple","score":0.5009791851043701},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4940700829029083},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.4649115204811096},{"id":"https://openalex.org/keywords/projection-pursuit","display_name":"Projection pursuit","score":0.4645844101905823},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.45239511132240295},{"id":"https://openalex.org/keywords/intrinsic-dimension","display_name":"Intrinsic dimension","score":0.4457300305366516},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.42669039964675903},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3653717637062073},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3443131744861603},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.256672739982605},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2000502347946167},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.11593908071517944},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.07906067371368408}],"concepts":[{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.7245786190032959},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.566650927066803},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5658774971961975},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5581303834915161},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5132222175598145},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5088089108467102},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5075434446334839},{"id":"https://openalex.org/C118930307","wikidata":"https://www.wikidata.org/wiki/Q600590","display_name":"Tuple","level":2,"score":0.5009791851043701},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4940700829029083},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.4649115204811096},{"id":"https://openalex.org/C118038509","wikidata":"https://www.wikidata.org/wiki/Q382970","display_name":"Projection pursuit","level":2,"score":0.4645844101905823},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45239511132240295},{"id":"https://openalex.org/C30732413","wikidata":"https://www.wikidata.org/wiki/Q17092636","display_name":"Intrinsic dimension","level":3,"score":0.4457300305366516},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42669039964675903},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3653717637062073},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3443131744861603},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.256672739982605},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2000502347946167},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.11593908071517944},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.07906067371368408},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/bigdata.2018.8622365","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622365","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1810.11562","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.11562","pdf_url":"https://arxiv.org/pdf/1810.11562","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2898557871","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1810.11562.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1810.11562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1810.11562","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1810.11562","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.11562","pdf_url":"https://arxiv.org/pdf/1810.11562","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2898557871.pdf","grobid_xml":"https://content.openalex.org/works/W2898557871.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W10600199","https://openalex.org/W188878397","https://openalex.org/W2004204172","https://openalex.org/W2015966799","https://openalex.org/W2022577532","https://openalex.org/W2027388439","https://openalex.org/W2028569884","https://openalex.org/W2030876210","https://openalex.org/W2031365860","https://openalex.org/W2033875152","https://openalex.org/W2060172488","https://openalex.org/W2072621406","https://openalex.org/W2093399550","https://openalex.org/W2115171184","https://openalex.org/W2123836401","https://openalex.org/W2133740040","https://openalex.org/W2187483593","https://openalex.org/W2813660609","https://openalex.org/W2885728209","https://openalex.org/W2950924875","https://openalex.org/W2951954998","https://openalex.org/W4238652859","https://openalex.org/W6607638216","https://openalex.org/W6632829569","https://openalex.org/W6686785280"],"related_works":["https://openalex.org/W1968683741","https://openalex.org/W2767856168","https://openalex.org/W2754478492","https://openalex.org/W1998660026","https://openalex.org/W1970647353","https://openalex.org/W1919402970","https://openalex.org/W2884063665","https://openalex.org/W3099588916","https://openalex.org/W329869691","https://openalex.org/W3034606471","https://openalex.org/W2935073713","https://openalex.org/W84198131","https://openalex.org/W3045299468","https://openalex.org/W3168563909","https://openalex.org/W2510641838","https://openalex.org/W3021132879","https://openalex.org/W3110761552","https://openalex.org/W2767026743","https://openalex.org/W3090020520","https://openalex.org/W3128511979"],"abstract_inverted_index":{"Dimensionality-reduction":[0],"methods":[1],"are":[2],"a":[3,46,70,87,95,148,164,177,195],"fundamental":[4],"tool":[5],"in":[6,33,104,188,198],"the":[7,16,19,23,30,61,64,76,100,105,114,117,123,139,153,158,170,191],"analysis":[8,79],"of":[9,22,42,63,80,93,116,125,141,168,179],"large":[10,81,173],"datasets.":[11,82],"These":[12],"algorithms":[13],"work":[14],"on":[15],"assumption":[17],"that":[18,73,92,97],"\"intrinsic":[20],"dimension\"":[21],"data":[24,44],"is":[25,36],"generally":[26],"much":[27],"smaller":[28],"than":[29],"ambient":[31],"dimension":[32,62,136],"which":[34],"it":[35],"collected.":[37],"Alongside":[38],"their":[39,183],"usual":[40],"purpose":[41],"mapping":[43],"into":[45],"smaller-dimensional":[47],"space":[48],"with":[49],"minimal":[50],"information":[51,59],"loss,":[52],"dimensionality-reduction":[53,88],"techniques":[54],"implicitly":[55],"or":[56],"explicitly":[57],"provide":[58,163],"about":[60],"dataset.In":[65],"this":[66,108,189],"paper,":[67,190],"we":[68,74,111,186],"propose":[69],"new":[71],"statistic":[72,197],"call":[75],"kappa-profile":[77,84,171,192],"for":[78,134,172],"The":[83],"arises":[85],"from":[86,121],"optimization":[89],"problem:":[90],"namely":[91],"finding":[94],"projection":[96,110],"optimally":[98],"preserves":[99],"secants":[101],"between":[102],"points":[103],"dataset.":[106],"From":[107],"optimal":[109],"extract":[112],"kappa,":[113],"norm":[115],"shortest":[118],"projected":[119],"secant":[120],"among":[122],"set":[124],"all":[126],"normalized":[127],"secants.":[128],"This":[129],"kappa":[130,142],"can":[131],"be":[132],"computed":[133],"any":[135],"k;":[137],"thus":[138,176],"tuple":[140],"values":[143],"(indexed":[144],"by":[145],"dimension)":[146],"becomes":[147],"kappa-profile.":[149],"Algorithms":[150],"such":[151],"as":[152,194],"Secant-Avoidance":[154,160],"Projection":[155,161],"algorithm":[156,162],"and":[157,175,181,206],"Hierarchical":[159],"computationally":[165],"feasible":[166],"means":[167],"estimating":[169],"datasets,":[174],"method":[178],"understanding":[180],"monitoring":[182],"behavior.":[184],"As":[185],"demonstrate":[187],"serves":[193],"useful":[196],"several":[199],"representative":[200],"settings:":[201],"weather":[202],"data,":[203,205],"soundscape":[204],"dynamical":[207],"systems":[208],"data.":[209]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}