A fast fixed-point BYY harmony learning algorithm on Gaussian mixture with automated model selection
Pattern Recognition Letters
Efficient Training of RBF Networks Via the BYY Automated Model Selection Learning Algorithms
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Convergence Behavior of Competitive Repetition-Suppression Clustering
Neural Information Processing
Image categorization via robust pLSA
Pattern Recognition Letters
Generalized competitive learning of Gaussian mixture models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
A greedy merge learning algorithm for Gaussian mixture model
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
A conditional random field model for image parsing
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Energy based competitive learning
Neurocomputing
Simultaneous model selection and feature selection via BYY harmony learning
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
The mahalanobis distance based rival penalized competitive learning algorithm
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
k'-Means algorithms for clustering analysis with frequency sensitive discrepancy metrics
Pattern Recognition Letters
Two-phase image segmentation with the competitive learning based chan-vese (CLCV) model
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Kernel k'-means algorithm for clustering analysis
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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Rival penalized competitive learning (RPCL) has been shown to be a useful tool for clustering on a set of sample data in which the number of clusters is unknown. However, the RPCL algorithm was proposed heuristically and is still in lack of a mathematical theory to describe its convergence behavior. In order to solve the convergence problem, we investigate it via a cost-function approach. By theoretical analysis, we prove that a general form of RPCL, called distance-sensitive RPCL (DSRPCL), is associated with the minimization of a cost function on the weight vectors of a competitive learning network. As a DSRPCL process decreases the cost to a local minimum, a number of weight vectors eventually fall into a hypersphere surrounding the sample data, while the other weight vectors diverge to infinity. Moreover, it is shown by the theoretical analysis and simulation experiments that if the cost reduces into the global minimum, a correct number of weight vectors is automatically selected and located around the centers of the actual clusters, respectively. Finally, we apply the DSRPCL algorithms to unsupervised color image segmentation and classification of the wine data