Tracking Concept Drifting with Gaussian Mixture Model

摘要

This paper mainly addresses the issue of semantic concept drifting in temporal sequences, such as video streams, over an extended period of time. Gaussian Mixture Model (GMM) is applied to model the distribution of under-investigating data, which are supposed to arrive or be generated in batches over time. The up-to-date classifier, which tracks the drifting concept, is directly built on the outdated models trained from the old labeled data. A couple of properties, such as Maximum Lifecycle, Dominant Component, Component Drifting Speed, System Stability, and Updating Speed, are defined to track concept drifting in the learning system, which is applied to determine corresponding parameters for model updating in order to obtain optimal up-to-date classifier. Experiments on simulated data and real-world data demonstrate that our proposed GMM-based batch learning framework is effective and efficient for dealing with concept drifting.