Finding Frequent Entities in Continuous Data
| Autor: | Rohan Chitnis, Leslie Pack Kaelbling, Tomás Lozano-Pérez, Ferran Alet |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer science Computer Science - Artificial Intelligence Feature vector Machine Learning (stat.ML) 02 engineering and technology Space (commercial competition) computer.software_genre Small set Continuous data 020901 industrial engineering & automation Artificial Intelligence (cs.AI) Statistics - Machine Learning Computer Science - Data Structures and Algorithms 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Fraction (mathematics) Data Structures and Algorithms (cs.DS) Data mining Online algorithm Cluster analysis computer |
| Zdroj: | Scopus-Elsevier MIT web domain IJCAI |
| Popis: | © 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. |
| Databáze: | OpenAIRE |
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