Alternative learning vector quantization
Pattern Recognition
Expansive competitive learning for kernel vector quantization
Pattern Recognition Letters
Clustering: A neural network approach
Neural Networks
Phonetically-driven CELP coding using self-organizing maps
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Suppressed fuzzy-soft learning vector quantization for MRI segmentation
Artificial Intelligence in Medicine
Classification of 3-D objects and faces employing view-based clusters
Computers and Electrical Engineering
| Hi-index | 35.68 |
The authors provide a convergence analysis for the Kohonen learning algorithm (KLA) with respect to vector quantizer (VQ) optimality criteria and introduce a stochastic relaxation technique which produces the global minimum but is computationally expensive. By incorporating the principles of the stochastic approach into the KLA, a deterministic VQ design algorithm, the soft competition scheme (SCS), is introduced. Experimental results are presented where the SCS consistently provided better codebooks than the generalized Lloyd algorithm (GLA), even when the same computation time was used for both algorithms. The SCS may therefore prove to be a valuable alternative to the GLA for VQ design