Finding Frequent Entities in Continuous Data
Name
alet-ijcai18.pdf
Description
Accepted version
Size
3.49 MB
Format
Adobe PDF
Checksum (MD5)
04f33aaa9ef10db73514d6f4d5a84e52
Author(s)
Alet, Ferran
Chitnis, Rohan
Kaelbling, Leslie P.
Lozano-Perez, Tomas
Date Issued
July 2018
Publisher
International Joint Conferences on Artificial Intelligence Organization
Citation
Alet, Ferran, Chitnis, Rohan, Kaelbling, Leslie P. and Lozano-Perez, Tomas. 2018. "Finding Frequent Entities in Continuous Data."
Version
Author's final manuscript
Abstract
© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.24963/ijcai.2018/275
