{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/41603"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/41603","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Cross-domain self organizing maps","abstract":"In this thesis, I present a method for organizing and relating events represented in two domains: the transition-space domain, which focuses on change and the trajectory-space domain, which focuses on movement along paths. Particular events are described in both domains, and each description is fed into a self organizing map. After these self organizing maps have been trained with enough events, the maps are clustered independently. Then, after the two self organizing maps are clustered, the clusters in the two maps are themselves clustered, creating links between trajectory descriptions and the transition descriptions. Thus, I provide a method for relating events seen in multiple perspectives. After training with 1914 different sentences about motion, my implemented system noted that particular motions along a path are highly correlated with particular transitions. For example, \"the bird flew to the top of a tree\" is part of a trajectory cluster that is highly correlated with a transition cluster in which a motion appears and a distance first decreases and finally disappears.","abstract_html":"In this thesis, I present a method for organizing and relating events represented in two domains: the transition-space domain, which focuses on change and the trajectory-space domain, which focuses on movement along paths. Particular events are described in both domains, and each description is fed into a self organizing map. After these self organizing maps have been trained with enough events, the maps are clustered independently. Then, after the two self organizing maps are clustered, the clusters in the two maps are themselves clustered, creating links between trajectory descriptions and the transition descriptions. Thus, I provide a method for relating events seen in multiple perspectives. After training with 1914 different sentences about motion, my implemented system noted that particular motions along a path are highly correlated with particular transitions. For example, "the bird flew to the top of a tree" is part of a trajectory cluster that is highly correlated with a transition cluster in which a motion appears and a distance first decreases and finally disappears.","abstract_has_math":false,"creators":["Fidelholtz, Estanislao L"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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After training with 1914 different sentences about motion, my implemented system noted that particular motions along a path are highly correlated with particular transitions. For example, \"the bird flew to the top of a tree\" is part of a trajectory cluster that is highly correlated with a transition cluster in which a motion appears and a distance first decreases and finally disappears."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Cross-domain self organizing maps"]}]}],"canonical_facts":{"dc:contributor.advisor":["Patrick H. Winston."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. 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Then, after the two self organizing maps are clustered, the clusters in the two maps are themselves clustered, creating links between trajectory descriptions and the transition descriptions. Thus, I provide a method for relating events seen in multiple perspectives. After training with 1914 different sentences about motion, my implemented system noted that particular motions along a path are highly correlated with particular transitions. For example, \"the bird flew to the top of a tree\" is part of a trajectory cluster that is highly correlated with a transition cluster in which a motion appears and a distance first decreases and finally disappears."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/41603"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. 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