Incremental Learning of Gaussian Mixture Models
نویسنده
چکیده
Gaussian Mixture Modeling (GMM) is a parametric method for high dimensional density estimation. Incremental learning of GMM is very important in problems such as clustering of streaming data and robot localization in dynamic environments. Traditional GMM estimation algorithms like EM Clustering tend to be computationally very intensive in these scenarios. We present an incremental GMM estimation algorithm which improves the proposed solution by Song et al [4] by improving the underlying EM algorithm for accuracy and performance. Another novel aspect of the proposed approach is that the statistical equivalence among different components is established through Bregman divergence which is known to be very efficient and optimal for variety of distribution functions belonging to exponential family.
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