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Online Gaussian Mixture Model for Concept Modeling and Discovery

Concept discovery and modeling are fundamental problems in machine learning research. Real world concepts are usually high-dimensional and have complicated distributions along their dimensions. Gaussian Mixture Models (GMM) have proved useful in modeling such complicated distributions. We propose a data-driven concept modeling and discovery framework using GMM, with on-line updating mechanism for fast computation suitable for real world applications. Experiments show the efficacy and efficiency of the proposed algorithm.