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31 July 2002 Optimal multidimensional quantization for pattern recognition
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Proceedings Volume 4875, Second International Conference on Image and Graphics; (2002)
Event: Second International Conference on Image and Graphics, 2002, Hefei, China
In non-parametric pattern recognition, the probability density function is approximated by means of many parameters, each one for a density value in a small hyper-rectangular volume of the space. The hyper-rectangles are determined by appropriately quantizing the range of each variable. Optimal quantization determines a compact and efficient representation of the probability density of data by optimizing a global quantizer performance measure. The measure used here is a weighted combination of average log likelihood, entropy and correct classification probability. In multi-dimensions, we study a grid based quantization technique. Smoothing is an important aspect of optimal quantization because it affects the generalization ability of the quantized density estimates. We use a fast generalized k nearest neighbor smoothing algorithm. We illustrate the effectiveness of optimal quantization on a set of not very well separated Gaussian mixture models, as compared to the expectation maximization (EM) algorithm. Optimal quantization produces better results than the EM algorithm. The reason is that the convergence of the EM algorithm to the true parameters for not well separated mixture models can be extremely slow.
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Mingzhou Song and Robert M. Haralick "Optimal multidimensional quantization for pattern recognition", Proc. SPIE 4875, Second International Conference on Image and Graphics, (31 July 2002);

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