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Proceedings Paper

Tracking concept drifting with Gaussian mixture model
Author(s): Jun Wu; Xian-Sheng Hua; Bo Zhang
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Paper Abstract

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 The up-to-date classifier, which tracks the drifting concept, is directly built on the outdated models trained from the old labeled data.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.

Paper Details

Date Published: 24 June 2005
PDF: 9 pages
Proc. SPIE 5960, Visual Communications and Image Processing 2005, 59604L (24 June 2005); doi: 10.1117/12.632730
Show Author Affiliations
Jun Wu, Tsinghua Univ. (China)
Xian-Sheng Hua, Microsoft Research Asia (China)
Bo Zhang, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 5960:
Visual Communications and Image Processing 2005
Shipeng Li; Fernando Pereira; Heung-Yeung Shum; Andrew G. Tescher, Editor(s)

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