Finding important entities in continuous streaming data
Name
1051458612-MIT.pdf
Description
Full printable version
Size
6.56 MB
Format
Adobe PDF
Checksum (MD5)
5beaea5f1dc66f55649ae79433f6d22b
Author(s)
Alet, Ferran (Alet I Puig)
Advisor(s)
Leslie P. Kaelbling and Tomás Lozano-Pérez.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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