Advances in neural information processing systems 2
Quantitative measures of change based on feature organization: eigenvalues and eigenvectors
Computer Vision and Image Understanding
A randomized algorithm for pairwise clustering
Proceedings of the 1998 conference on Advances in neural information processing systems II
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
A Maximum Likelihood Framework for Grouping and Segmentation
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
SMEM Algorithm for Mixture Models
Neural Computation
A kurtosis-based dynamic approach to Gaussian mixture modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Optical flow estimation and moving object segmentation based on median radial basis function network
IEEE Transactions on Image Processing
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In this paper we provide a direct link between the EM algorithm and matrix factorisation methods for grouping via pairwise clustering. We commence by placing the pairwise clustering process in the setting of the EM algorithm. We represent the clustering process using two sets of variables which need to be estimated. The first of these are cluster-membership indicators. The second are revised link-weights between pairs of nodes. We work with a model of the grouping process in which both sets of variables are drawn from a Bernoulli distribution. The main contributioin in this paper is to show how the cluster-memberships may be estimated using the leading eigenvector of the revised link-weight matrices. We also establish convergence conditions for the resulting pairwise clustering process. The method is demonstrated on the problem of multiple moving object segmentation.