{"id":"https://openalex.org/W2107939926","doi":null,"title":"Manifold Preserving Hierarchical Topic Models for Quantization and Approximation","display_name":"Manifold Preserving Hierarchical Topic Models for Quantization and Approximation","publication_year":2013,"publication_date":"2013-06-16","ids":{"openalex":"https://openalex.org/W2107939926","mag":"2107939926"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.421.9047","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.421.9047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://jmlr.org/proceedings/papers/v28/kim13a.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5064582903","display_name":"Minje Kim","orcid":"https://orcid.org/0000-0003-3513-8328","observed_orcids":["https://orcid.org/0000-0003-3513-8328"]},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Minje Kim","raw_affiliation_strings":["Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"],"raw_orcid":"https://orcid.org/0000-0003-3513-8328","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038903729","display_name":"Paris Smaragdis","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]},{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Paris Smaragdis","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA","Adobe Research, Adobe Systems Inc., San Francisco, CA 94103, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA","institution_ids":["https://openalex.org/I157725225"]},{"raw_affiliation_string":"Adobe Research, Adobe Systems Inc., San Francisco, CA 94103, USA","institution_ids":["https://openalex.org/I1306409833"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8234,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.84789871,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1373","last_page":"1381"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.492900013923645,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.492900013923645,"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"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.27320000529289246,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.09390000253915787,"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/hierarchical-topic-models","display_name":"hierarchical topic models","score":0.875},{"id":"https://openalex.org/keywords/manifold-learning","display_name":"manifold learning","score":0.7049999833106995},{"id":"https://openalex.org/keywords/data-quantization","display_name":"data quantization","score":0.6290000081062317},{"id":"https://openalex.org/keywords/latent-variable-modeling","display_name":"latent variable models","score":0.6330000162124634},{"id":"https://openalex.org/keywords/handwritten-digit-recognition","display_name":"handwritten digit recognition","score":0.8600000143051147},{"id":"https://openalex.org/keywords/speech-separation","display_name":"speech separation","score":0.996999979019165}],"concepts":[{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.6616650223731995},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6373705267906189},{"id":"https://openalex.org/C153120616","wikidata":"https://www.wikidata.org/wiki/Q17068315","display_name":"Manifold alignment","level":4,"score":0.6256862878799438},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.621107816696167},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.5335367918014526},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5170212984085083},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.4930882155895233},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4133411645889282},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39584967494010925},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39242255687713623},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37872427701950073},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.3721292316913605},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08785253763198853},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.08580648899078369},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.421.9047","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.421.9047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://jmlr.org/proceedings/papers/v28/kim13a.pdf","raw_type":"text"},{"id":"mag:2107939926","doi":null,"is_oa":false,"landing_page_url":"http://paris.cs.illinois.edu/pubs/kim-icml2013.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306419644","display_name":"International Conference on Machine Learning","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":false,"raw_source_name":"International Conference on Machine Learning","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1612003148","https://openalex.org/W1880262756","https://openalex.org/W1902027874","https://openalex.org/W2039844283","https://openalex.org/W2053186076","https://openalex.org/W2097129520","https://openalex.org/W2107743791","https://openalex.org/W2112796928","https://openalex.org/W2117150818","https://openalex.org/W2127851351","https://openalex.org/W2129004009","https://openalex.org/W2135151673","https://openalex.org/W3127686677"],"related_works":["https://openalex.org/W2901107046","https://openalex.org/W2026967148","https://openalex.org/W2990705308","https://openalex.org/W7030033079","https://openalex.org/W2334131520","https://openalex.org/W2522052052","https://openalex.org/W2208835321","https://openalex.org/W1562553690","https://openalex.org/W2097340099","https://openalex.org/W4310283082"],"abstract_inverted_index":{"We":[0,76],"present":[1],"two":[2],"complementary":[3],"topic":[4],"models":[5,82],"to":[6,45],"address":[7,46],"the":[8,36,40,47,52,63,78,86,91],"analysis":[9],"of":[10,39,49,55,62,80],"mixture":[11],"data":[12,31],"lying":[13],"on":[14,85],"manifolds.":[15],"First,":[16],"we":[17,65],"propose":[18],"a":[19,67,72],"quantization":[20],"method":[21],"with":[22,83],"an":[23],"additional":[24],"midlayer":[25],"latent":[26],"variable,":[27],"which":[28],"selects":[29],"only":[30],"points":[32],"that":[33,56,70],"best":[34],"preserve":[35],"manifold":[37,57],"structure":[38],"input":[41],"data.":[42],"In":[43],"order":[44],"case":[48],"modeling":[50],"all":[51],"in-between":[53],"parts":[54],"using":[58],"this":[59],"reduced":[60],"representation":[61],"input,":[64],"introduce":[66],"new":[68],"model":[69],"provides":[71],"manifold-aware":[73],"interpolation":[74],"method.":[75],"demonstrate":[77],"advantages":[79],"these":[81],"experiments":[84],"hand-written":[87],"digit":[88],"recognition":[89],"and":[90],"speech":[92],"source":[93],"separation":[94],"tasks.":[95],"1.":[96]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":2}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
