{"id":"https://openalex.org/W2781866350","doi":"https://doi.org/10.1145/3150919.3150925","title":"Improved Locally Linear Embedding for Big-data Classification","display_name":"Improved Locally Linear Embedding for Big-data Classification","publication_year":2017,"publication_date":"2017-11-07","ids":{"openalex":"https://openalex.org/W2781866350","doi":"https://doi.org/10.1145/3150919.3150925","mag":"2781866350"},"language":"en","primary_location":{"id":"doi:10.1145/3150919.3150925","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3150919.3150925","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102720776","display_name":"Andr\u00e9s Ramirez","orcid":"https://orcid.org/0000-0001-6273-5211"},"institutions":[{"id":"https://openalex.org/I96749437","display_name":"Texas A&M University \u2013 Corpus Christi","ror":"https://ror.org/01mrfdz82","country_code":"US","type":"education","lineage":["https://openalex.org/I96749437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andres Ramirez","raw_affiliation_strings":["Texas A&amp;M University-Corpus Christi, Corpus Christi, Tx"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas A&amp;M University-Corpus Christi, Corpus Christi, Tx","institution_ids":["https://openalex.org/I96749437"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010792548","display_name":"Maryam Rahnemoonfar","orcid":"https://orcid.org/0000-0001-9358-2836"},"institutions":[{"id":"https://openalex.org/I96749437","display_name":"Texas A&M University \u2013 Corpus Christi","ror":"https://ror.org/01mrfdz82","country_code":"US","type":"education","lineage":["https://openalex.org/I96749437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maryam Rahnemoonfar","raw_affiliation_strings":["Texas A&amp;M University-Corpus Christi, Corpus Christi, Tx"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas A&amp;M University-Corpus Christi, Corpus Christi, Tx","institution_ids":["https://openalex.org/I96749437"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I96749437"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.26839399,"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":"37","last_page":"41"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.9961000084877014,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9739000201225281,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/nonlinear-dimensionality-reduction","display_name":"Nonlinear dimensionality reduction","score":0.7988848686218262},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7794818878173828},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.6899641156196594},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6824703812599182},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6374542713165283},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6335581541061401},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6011036038398743},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5418153405189514},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5397504568099976},{"id":"https://openalex.org/keywords/manifold","display_name":"Manifold (fluid mechanics)","score":0.5046740770339966},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.41063764691352844},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3726844787597656}],"concepts":[{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.7988848686218262},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7794818878173828},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.6899641156196594},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6824703812599182},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6374542713165283},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6335581541061401},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6011036038398743},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5418153405189514},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5397504568099976},{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.5046740770339966},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.41063764691352844},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3726844787597656},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3150919.3150925","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3150919.3150925","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1563653928","https://openalex.org/W1574693922","https://openalex.org/W1980431326","https://openalex.org/W2006327073","https://openalex.org/W2037412776","https://openalex.org/W2053186076","https://openalex.org/W2064987453","https://openalex.org/W2096599785","https://openalex.org/W2099609584","https://openalex.org/W2329783221","https://openalex.org/W2480854438","https://openalex.org/W2596462923","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2375574759","https://openalex.org/W2132083814","https://openalex.org/W3088634662","https://openalex.org/W2292979300","https://openalex.org/W3162910294","https://openalex.org/W2539700568","https://openalex.org/W2383239174","https://openalex.org/W117517268","https://openalex.org/W2166963679","https://openalex.org/W2573981081"],"abstract_inverted_index":{"A":[0],"hyperspectral":[1,25],"image":[2],"provides":[3],"a":[4,56],"multidimensional":[5],"data":[6,26],"consisting":[7],"of":[8,10,18,24,33,39,55,97],"hundreds":[9],"spectral":[11,19],"dimensions.":[12],"Even":[13],"though":[14],"having":[15],"an":[16,100],"abundance":[17],"might":[20],"seem":[21],"favorable,":[22],"classification":[23,42],"tends":[27],"to":[28,72,80,87],"collide":[29],"with":[30],"the":[31,37,49,75,108],"curse":[32],"dimensionality.":[34],"Therefore,":[35],"reducing":[36],"number":[38],"dimensions":[40],"before":[41],"is":[43,74],"always":[44],"favorable.":[45],"For":[46],"this":[47,90,92],"research,":[48],"feature":[50],"extraction":[51],"method":[52],"will":[53],"consist":[54],"nonlinear":[57],"manifold":[58,82],"learning":[59,83],"technique":[60],"named":[61],"locally":[62],"linear":[63],"embedding":[64],"(LLE).":[65],"Additionally,":[66],"another":[67],"problem":[68],"that":[69,103],"we":[70],"attempt":[71],"overcome":[73,89],"high":[76],"computational":[77],"time":[78],"required":[79],"run":[81],"methods.":[84],"In":[85],"order":[86],"help":[88],"problem,":[91],"research":[93],"compares":[94],"one":[95],"implementation":[96],"LLE":[98],"against":[99],"improved":[101],"version":[102],"runs":[104],"much":[105],"quicker":[106],"than":[107],"original":[109],"version.":[110]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
