Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Competitive learning algorithms for vector quantization
Neural Networks
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Digital Pictures: Representation and Compression
Digital Pictures: Representation and Compression
The Journal of Supercomputing
Multi-grain parallel processing of data-clustering on programmable graphics hardware
ISPA'04 Proceedings of the Second international conference on Parallel and Distributed Processing and Applications
Hi-index | 14.98 |
Presents novel sequential and parallel learning techniques for codebook design in vector quantizers using neural network approaches. These techniques are used in the training phase of the vector quantizer design. These learning techniques combine the split-and-cluster methodology of the traditional vector quantizer design with neural learning, and lead to better quantizer design (with fewer distortions). The sequential learning approach overcomes the code word underutilization problem of the competitive learning network. As a result, this network only requires partial or zero updating, as opposed to full neighbor updating as needed in the self organizing feature map. The parallel learning network, while satisfying the above characteristics, also leads to parallel learning of the codewords. The parallel learning technique can be used for faster codebook design in a multiprocessor environment. It is shown that this sequential learning scheme can sometimes outperform the traditional LBG algorithm, while the parallel learning scheme performs very close to the LGB and the sequential learning algorithms.