{"id":"https://openalex.org/W2095961686","doi":"https://doi.org/10.1109/cidm.2013.6597242","title":"Interpretable magnification factors for topographic maps of high dimensional and structured data","display_name":"Interpretable magnification factors for topographic maps of high dimensional and structured data","publication_year":2013,"publication_date":"2013-04-01","ids":{"openalex":"https://openalex.org/W2095961686","doi":"https://doi.org/10.1109/cidm.2013.6597242","mag":"2095961686"},"language":"en","primary_location":{"id":"doi:10.1109/cidm.2013.6597242","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cidm.2013.6597242","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","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/A5060507310","display_name":"Nikolaos Gianniotis","orcid":"https://orcid.org/0000-0002-2187-5522"},"institutions":[{"id":"https://openalex.org/I176453806","display_name":"University of Potsdam","ror":"https://ror.org/03bnmw459","country_code":"DE","type":"education","lineage":["https://openalex.org/I176453806"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Nikolaos Gianniotis","raw_affiliation_strings":["Institute of Earth and Environmental Science, University of Potsdam, Potsdam, Germany","Institute of Earth and Environmental Science, University of Potsdam, Karl-Liebknecht-Strasse 24-25, 14476, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Earth and Environmental Science, University of Potsdam, Potsdam, Germany","institution_ids":["https://openalex.org/I176453806"]},{"raw_affiliation_string":"Institute of Earth and Environmental Science, University of Potsdam, Karl-Liebknecht-Strasse 24-25, 14476, Germany","institution_ids":["https://openalex.org/I176453806"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5060507310"],"corresponding_institution_ids":["https://openalex.org/I176453806"],"apc_list":null,"apc_paid":null,"fwci":0.8165,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.77506608,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"16","issue":null,"first_page":"238","last_page":"245"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9986000061035156,"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"}},"topics":[{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9986000061035156,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9939000010490417,"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/T10320","display_name":"Neural Networks and Applications","score":0.9825999736785889,"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/magnification","display_name":"Magnification","score":0.7694878578186035},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7501833438873291},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.7098925113677979},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6760645508766174},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.630568265914917},{"id":"https://openalex.org/keywords/multidimensional-scaling","display_name":"Multidimensional scaling","score":0.6165738105773926},{"id":"https://openalex.org/keywords/data-visualization","display_name":"Data visualization","score":0.5536367893218994},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.4930112063884735},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4399118423461914},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40608271956443787},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3875775933265686},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3600299060344696},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.19993287324905396},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19733324646949768},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09401300549507141},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.090766042470932},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.074490487575531},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.07140564918518066}],"concepts":[{"id":"https://openalex.org/C4144372","wikidata":"https://www.wikidata.org/wiki/Q675287","display_name":"Magnification","level":2,"score":0.7694878578186035},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7501833438873291},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.7098925113677979},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6760645508766174},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.630568265914917},{"id":"https://openalex.org/C91682802","wikidata":"https://www.wikidata.org/wiki/Q620538","display_name":"Multidimensional scaling","level":2,"score":0.6165738105773926},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.5536367893218994},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.4930112063884735},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4399118423461914},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40608271956443787},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3875775933265686},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3600299060344696},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.19993287324905396},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19733324646949768},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09401300549507141},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.090766042470932},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.074490487575531},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.07140564918518066},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cidm.2013.6597242","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cidm.2013.6597242","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321114","display_name":"Bundesministerium f\u00fcr Bildung und Forschung","ror":"https://ror.org/04pz7b180"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W36888928","https://openalex.org/W168937878","https://openalex.org/W837442027","https://openalex.org/W1556747266","https://openalex.org/W1981124406","https://openalex.org/W2002597011","https://openalex.org/W2007572995","https://openalex.org/W2043508111","https://openalex.org/W2054111008","https://openalex.org/W2079211475","https://openalex.org/W2093693172","https://openalex.org/W2100655604","https://openalex.org/W2107636931","https://openalex.org/W2122538988","https://openalex.org/W2125027820","https://openalex.org/W2128250382","https://openalex.org/W2134312057","https://openalex.org/W2140130790","https://openalex.org/W2142011304","https://openalex.org/W2146610201","https://openalex.org/W2163825541","https://openalex.org/W2169779569","https://openalex.org/W2949683059","https://openalex.org/W2949921041","https://openalex.org/W3016005719","https://openalex.org/W3120740533","https://openalex.org/W6601529611","https://openalex.org/W6623428372","https://openalex.org/W6678358927","https://openalex.org/W6680718068","https://openalex.org/W6684785420"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2088595421","https://openalex.org/W4241372895","https://openalex.org/W2106297084"],"abstract_inverted_index":{"Visualisation":[0],"of":[1,95,119,191],"high-dimensional":[2,13,49],"data":[3,25,42,113,140],"is":[4,22,122,169],"typically":[5],"formulated":[6],"as":[7,180],"a":[8,12,16,125,150],"non-linear":[9],"mapping":[10],"between":[11,139],"space":[14,50],"and":[15,73,103,133,171,187],"two-dimensional":[17],"latent":[18,35,60,78,101],"space.":[19,36,61,79],"The":[20,117,167],"goal":[21,118],"that":[23,44,110,129,154],"similar":[24,31],"items":[26,43,114,141],"should":[27],"be":[28,52,66,85],"projected":[29,53],"to":[30,39,55,70,87,89,123,163],"coordinates":[32],"in":[33,47,58,68,75,98],"the":[34,40,48,59,76,99,136,156,183,185],"Nevertheless,":[37],"due":[38],"non-linearity":[41],"are":[45,92,115],"distant":[46],"may":[51,83],"close":[54],"each":[56],"other":[57],"Therefore,":[62],"magnification":[63,81,165],"factors":[64,82],"must":[65],"analysed":[67],"order":[69],"detect":[71],"stretches":[72],"contractions":[74],"embedded":[77,100],"However,":[80],"not":[84,93],"straightforward":[86],"communicate":[88],"practitioners":[90],"who":[91],"aware":[94],"such":[96,179],"manifestations":[97],"space,":[102],"only":[104],"care":[105],"about":[106],"an":[107,146],"accurate":[108],"depiction":[109],"shows":[111],"which":[112],"similar.":[116],"this":[120],"work":[121],"devise":[124],"more":[126,142],"convenient":[127],"visualisation":[128,177],"corrects":[130,155],"for":[131],"magnifications":[132],"thus":[134],"depicts":[135],"true":[137],"distances":[138],"faithfully.":[143],"We":[144],"present":[145],"approach":[147,168],"based":[148],"on":[149,175,188],"multidimensional":[151],"scaling":[152],"technique":[153],"obtained":[157],"visualisations":[158],"by":[159],"distorting":[160],"them":[161],"according":[162],"local":[164],"factors.":[166],"general":[170],"we":[172],"demonstrate":[173],"it":[174],"different":[176,189],"algorithms,":[178],"GTM":[181],"extensions,":[182],"autoencoder,":[184],"GP-LVM,":[186],"types":[190],"data.":[192]},"counts_by_year":[{"year":2014,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}