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Vector quantization by deterministic annealing

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3 Author(s)
Rose, K. ; Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA ; Gurewitz, E. ; Fox, G.C.

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

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Information Theory, IEEE Transactions on  (Volume:38 ,  Issue: 4 )