Finding Frequent Entities in Continuous Data
This addresses the need for efficient entity identification in continuous data applications, such as video analysis and household monitoring, offering an incremental improvement over clustering methods.
The paper tackled the problem of identifying frequent entities in continuous high-dimensional data by formalizing it as a heavy hitters problem instead of clustering, resulting in more accurate and effective solutions, with a novel online algorithm (HAC) demonstrated on real video and household domains.
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.