Finding important entities in continuous streaming data
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/118027 |
| _version_ | 1811096791508058112 |
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| author | Alet, Ferran (Alet I Puig) |
| author2 | Leslie P. Kaelbling and Tomás Lozano-Pérez. |
| author_facet | Leslie P. Kaelbling and Tomás Lozano-Pérez. Alet, Ferran (Alet I Puig) |
| author_sort | Alet, Ferran (Alet I Puig) |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T16:49:04Z |
| format | Thesis |
| id | mit-1721.1/118027 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:49:04Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1180272019-04-10T09:19:07Z Finding important entities in continuous streaming data Alet, Ferran (Alet I Puig) Leslie P. Kaelbling and Tomás Lozano-Pérez. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 65-67). In many applications that involve processing high-dimensional data, it is important to identify a small set of entities that account for a significant fraction of detections. Rather than formalize this as a clustering problem, in which all detections must be grouped into hard or soft categories, we formalize it as an instance of the frequent items or heavy hitters problem, which finds groups of tightly clustered objects that have a high density in the feature space. We show that the heavy hitters formulation generates solutions that are more accurate and effective than the clustering formulation. In addition, we present a novel online algorithm for heavy hitters, called HAC, which addresses problems in continuous space, and demonstrate its effectiveness on real video and household domains. by Ferran Alet. S.M. 2018-09-17T15:54:24Z 2018-09-17T15:54:24Z 2018 2018 Thesis http://hdl.handle.net/1721.1/118027 1051458612 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 67 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Alet, Ferran (Alet I Puig) Finding important entities in continuous streaming data |
| title | Finding important entities in continuous streaming data |
| title_full | Finding important entities in continuous streaming data |
| title_fullStr | Finding important entities in continuous streaming data |
| title_full_unstemmed | Finding important entities in continuous streaming data |
| title_short | Finding important entities in continuous streaming data |
| title_sort | finding important entities in continuous streaming data |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/118027 |
| work_keys_str_mv | AT aletferranaletipuig findingimportantentitiesincontinuousstreamingdata |