Finding Frequent Entities in Continuous Data

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...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Alet, Ferran, Chitnis, Rohan, Kaelbling, Leslie P, Lozano-Perez, Tomas
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 08.05.2018
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary: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.
ISSN:2331-8422