Online Learning of Gaussian Mixture Models - a Two-Level Approach

By Arnaud Declercq, Justus H. Piater. 2008

75 citations recorded by the source corpus.

We present a method for incrementally learning mixture models that avoids the necessity to keep all data points around. It contains a single user-settable parameter that controls via a novel statistical criterion the trade-off between the number of mixture components and the accuracy of representing the data. A key idea is that each component of the (non-overfitting) mixture is in turn represented by an underlying mixture that represents the data very precisely (without regards to overfitting); this allows the model to be refined without sacrificing accuracy.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Lifelong Generative Modeling (2017, methodology): “Past work in sequential learning of generative models has focused on learning Gaussian mixture models Singer & Warmuth (1999); Declercq & Piater (2008) or on variational methods such as Variational EM Ghahramani & Attias (2000).”
ECO: Efficient Convolution Operators for Tracking (2016, methodology): “To update the GMM, we use a simplified version of the online algorithm by Declercq and Piater [14].”
Image-based plant phenotyping with incremental learning and active contours (2014, methodology): “To eliminate the need to store the collection of all feature vectors x for all seen images, we update the GMM after each segmented image in an online fashion as in Declercq and Piater (2008).”