Efficient parallel processing of competitive learning algorithms
Parallel Computing
An efficient line symmetry-based K-means algorithm
Pattern Recognition Letters
Sample-size adaptive self-organization map for color images quantization
Pattern Recognition Letters
High-speed codebook design by the multipath competitive learning on a systolic memory architecture
ISCGAV'04 Proceedings of the 4th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
A Practical Clustering Algorithm
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
High Capacity Reversible Data Hiding for 3D Meshes in the PVQ Domain
IWDW '07 Proceedings of the 6th International Workshop on Digital Watermarking
Revised PSK clustering algorithm
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Fast randomized algorithm for center-detection
Pattern Recognition
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The design of the optimal codebook for a given codebook size and input source is a challenging puzzle that remains to be solved. The key problem in optimal codebook design is how to construct a set of codevectors efficiently to minimize the average distortion. A minimax criterion of minimizing the maximum partial distortion is introduced in this paper. Based on the partial distortion theorem, it is shown that minimizing the maximum partial distortion and minimizing the average distortion will asymptotically have the same optimal solution corresponding to equal and minimal partial distortion. Motivated by the result, we incorporate the alternative minimax criterion into the on-line learning mechanism, and develop a new algorithm called minimax partial distortion competitive learning (MMPDCL) for optimal codebook design. A computation acceleration scheme for the MMPDCL algorithm is implemented using the partial distance search technique, thus significantly increasing its computational efficiency. Extensive experiments have demonstrated that compared with some well-known codebook design algorithms, the MMPDCL algorithm consistently produces the best codebooks with the smallest average distortions. As the codebook size increases, the performance gain becomes more significant using the MMPDCL algorithm. The robustness and computational efficiency of this new algorithm further highlight its advantages