A Principal Component Clustering Approach to Object-Oriented Motion Segmentation and Estimation
Journal of VLSI Signal Processing Systems - Special issue on recent development in video: algorithms, implementation and applications
FREM: fast and robust EM clustering for large data sets
Proceedings of the eleventh international conference on Information and knowledge management
Unsupervised Texture Segmentation Using Stochastic Version of the EM Algorithm and Data Fusion
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
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``Expectation-Maximization'''' (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix $P$, and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of $P$ and provide new results analyzing the effect that $P$ has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages and disadvantages of EM and other algorithms for the learning of Gaussian mixture models.