Competitive learning algorithms for vector quantization
Neural Networks
Neural Networks
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Self-organizing maps
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Soft Computing and Human-Centered Machines
Soft Computing and Human-Centered Machines
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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Clustering in the neural-network literature is generally based on the competitive learning paradigm[4]. This paper presents a new clustering algorithm which is against initialization while meantime can find the natural prototypes in the input data, especially it could partly handle problems that Rival Penalized Competitive Learning (RPCL) algorithm have. Simulation results on synthesized data sets show that proposed method is effective and robust. Application of the proposed robust RPCL algorithm in indexing of visual features is discussed.