Information cut for clustering using a gradient descent approach
Pattern Recognition
Engineering Applications of Artificial Intelligence
Expansive competitive learning for kernel vector quantization
Pattern Recognition Letters
Median fuzzy c-means for clustering dissimilarity data
Neurocomputing
Fundamenta Informaticae
Regularized discriminant entropy analysis
Pattern Recognition
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A deterministic annealing approach is suggested to search for the optimal vector quantizer given a set of training data. The problem is reformulated within a probabilistic framework. No prior knowledge is assumed on the source density, and the principle of maximum entropy is used to obtain the association probabilities at a given average distortion. The corresponding Lagrange multiplier is inversely related to the `temperature' and is used to control the annealing process. In this process, as the temperature is lowered, the system undergoes a sequence of phase transitions when existing clusters split naturally, without use of heuristics. The resulting codebook is independent of the codebook used to initialize the iterations