Online Gaussian Mixture Model for Concept Modeling and Discovery

نویسندگان

  • Chunsheng Fang
  • Anca L. Ralescu
چکیده

How to model a concept, and how to discover a new concept, remain fundamental in machine learning research. Real world concepts are usually high-dimensional and have complicated distributions. Gaussian Mixture Model has strength in modeling complicated distributions. In this paper, we propose a data-driven concept modeling and discovery framework using GMM, with online updating mechanism for fast computation in real world application. Experiments show that our proposed algorithm can handle complicated concepts modeling and discovery with satisfactory performance in real time.

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تاریخ انتشار 2008