Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Competitive learning algorithms for vector quantization
Neural Networks
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Vector quantization and signal compression
Vector quantization and signal compression
Color Image Segmentation using Competitive Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A self-organizing network for hyperellipsoidal clustering (HEC)
IEEE Transactions on Neural Networks
Improved Production of Competitive Learning Rules with an Additional Term for Vector Quantization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Convergence Behavior of Competitive Repetition-Suppression Clustering
Neural Information Processing
Expansive competitive learning for kernel vector quantization
Pattern Recognition Letters
Image compression by vector quantization with recurrent discrete networks
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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In this paper, we develop a necessary and sufficient condition for a local minimum to be a global minimum to the vector quantization problem and present a competitive learning algorithm based on this condition which has two learning terms; the first term regulates the force of attraction between the synaptic weight vectors and the input patterns in order to reach a local minimum while the second term regulates the repulsion between the synaptic weight vectors and the input's gravity center to favor convergence to the global minimum This algorithm leads to optimal or near optimal solutions and it allows the network to escape from local minima during training. Experimental results in image compression demonstrate that it outperforms the simple competitive learning algorithm, giving better codebooks.