Energy based competitive learning

Neurocomputing

Volumn 74, Issue 12-13, 2011, Pages 2265-2275

  Wang, Chang Dong ^a   Lai, Jiang Huang ^a  

^a SUN YAT SEN UNIVERSITY   (China)

Author keywords

Auto initialization; Competitive learning; Energy; Learning rate; Outliers; Size sparsity

Indexed keywords

AUTO INITIALIZATIONS; COMPETITIVE LEARNING; ENERGY; LEARNING RATE; OUTLIERS; SIZE-SPARSITY;

CLUSTERING ALGORITHMS; COMPETITION; EXTRACTION; LEARNING ALGORITHMS; STATISTICS;

IMAGE SEGMENTATION;

ADAPTIVE LEARNING RATE BASED ON SAMPLE ENERGY; ANALYTIC METHOD; ARTICLE; CLUSTER ANALYSIS; CONTROLLED STUDY; DATA ANALYSIS; DATA BASE; ENERGY BASED COMPETITIVE LEARNING; IMAGE DISPLAY; IMAGE PROCESSING; LEARNING ALGORITHM; PRIORITY JOURNAL; PROBLEM SOLVING; SAMPLING; SIMULATION; SIZE SPARSITY BALANCE OF CLUSTER; STATISTICAL PARAMETERS; STATISTICS;

EID: 79956131625     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.03.013     Document Type: Article

References (27)

1. Rumelhart D.E., Zipser D. Feature discovery by competitive learning. Cognitive Science 1985, 9:75-112.
2. Xu L. A unified perspective and new results on RHT computing, mixture based learning, and multi-learner based problem solving. Pattern Recognition 2007, 40:2129-2153.
3. Xu L., Krzyzak A., Oja E. Rival penalized competitive learning for clustering analysis, RBF net, and curve detection. IEEE Transactions on Neural Networks 1993, 4:636-649.
4. Hwang W.-J., Ye B.-Y., Liao S.-C. A novel entropy-constrained competitive learning algorithm for vector quantization. Neurocomputing 1999, 25:133-147.
5. Xu L. RBF nets, mixture experts, and Bayesian Ying-Yang learning. Neurocomputing 1998, 19:223-257.
6. Liu Z.-Y., Chiu K.-C., Xu L. Strip line detection and thinning by RPCL-based local PCA. Pattern Recognition Letters 2003, 24:2335-2344.
7. Liu Z.-Y., Qiao H., Xu L. Multisets mixture learning-based ellipse detection. Pattern Recognition 2006, 39:731-735.
8. Cheung Y.-M., Xu L. Rival penalized competitive learning based approach for discrete-valued source separation. International Journal on Neural Systems 2000, 10:483-490.
9. Cheung Y.-M., Xu L. An RPCL-based approach for Markov model identification with unknown state number. IEEE Signal Processing Letters 2000, 7:284-287.
10. Liu Z.-Y., Xu L. Topological local principal component analysis. Neurocomputing 2003, 55:739-745.
11. Chen R.-M., Huang Y.-M. Competitive neural network to solve scheduling problems. Neurocomputing 2001, 37:177-196.
12. Xu L. Rival penalized competitive learning, finite mixture, and multisets clustering. Proceedings of the International Joint Conference on Neural Networks (IJCNN 1998) 1998.
13. Ma J., Wang T., Xu L. A gradient BYY harmony learning rule on Gaussian mixture with automated model selection. Neurocomputing 2004, 56:481-487.
14. Ma J., Xu L. Asymptotic convergence properties of the EM algorithm with respect to the overlap in the mixture. Neurocomputing 2005, 68:105-129.
15. Ma J., Wang T. A cost-function approach to rival penalized competitive learning (RPCL). Cybernetics Part B, Cybernetics 2006, 36:722-737.
16. Cheung Y.-M. On rival penalization controlled competitive learning for clustering with automatic cluster number selection. IEEE Transactions on Knowledge Data Engineering 2005, 17:1583-1588.
17. Zhang Y.-J., Liu Z.-Q. Self-splitting competitive learning: a new on-line clustering paradigm. IEEE Transactions on Neural Networks 2002, 13:369-380.
18. Bacciu D., Starita A. Competitive repetition suppression clustering: a biologically inspired learning model with application to robust clustering. IEEE Transactions on Neural Networks 2008, 19:1922-1941.
19. Xu L. BYY harmony learning, structural RPCL, and topological self-organizing on mixture models. Neural Networks 2002, 15:1125-1151.
20. Lu Z., Ip H. Generalized competitive learning of Gaussian mixture models. Cybernetics Part B, Cybernetics 2009, 39:901-909.
21. Bishop C.M. Pattern Recognition and Machine Learning 2006, Springer.
22. Ester M., Kriegel H.-P., Sander J., Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 1996) 1996.
23. Ertöz L., Steinbach M., Kumar V. Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data. SIAM International Conference on Data Mining (SDM 2003) 2003.
24. DeSieno D. Adding a conscience to competitive learning. IEEE International Conference on Neural Network (ICNN 1988) 1988, 117-124.
25. Martin D., Fowlkes C., Tal D., Malik J. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. IEEE Conference on ICCV 2001, vol. 2:416-423.
26. Unnikrishnan R., Pantofaru C., Hebert M. Toward objective evaluation of image segmentation algorithms. IEEE Transactions Pattern on Analysis and Machine Intelligence 2007, 29:929-944.
27. Fowlkes J.M.C., Martin D. Local figure-ground cues are valid for natural images. Journal of Vision 2007, 7:1-9.