Fuzzy declustering-based vector quantization

Authors:
Tuan D. Pham;Miriam Brandl;Dominik Beck
Affiliations:
School of Information Technology and Electrical Engineering, The University of New South Wales, ADFA, Canberra, ACT 2600, Australia;School of Information Technology and Electrical Engineering, The University of New South Wales, ADFA, Canberra, ACT 2600, Australia;School of Information Technology and Electrical Engineering, The University of New South Wales, ADFA, Canberra, ACT 2600, Australia
Venue:
Pattern Recognition
Year:
2009

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Abstract

Vector quantization is a useful approach for multi-dimensional data compression and pattern classification. One of the most popular techniques for vector quantization design is the LBG (Linde, Buzo, Gray) algorithm. To address the problem of producing poor estimate of vector centroids which are subjected to biased data in vector quantization; we propose a fuzzy declustering strategy for the LBG algorithm. The proposed technique calculates appropriate declustering weights to adjust the global data distribution. Using the result of fuzzy declustering-based vector quantization design, we incorporate the notion of fuzzy partition entropy into the distortion measures that can be useful for classification of spectral features. Experimental results obtained from simulated and real data sets demonstrate the effective performance of the proposed approach.