Selection of the suitable neighborhood size based on Bayesian Information Criterion

3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice

Volumn 2, Issue , 2010, Pages 367-371

  Shao, Chao ^a   Zhang, Bin ^a  

^a HENAN UNIVERSITY OF ECONOMICS AND LAW   (China)

Author keywords

BIC; Kmeans; Manifold learning; Residual variance; The neighborhood size

Indexed keywords

BIC; K-MEANS; MANIFOLD LEARNING; NEIGHBORHOOD SIZE; RESIDUAL VARIANCE;

BAYESIAN NETWORKS; ERRORS; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; REPAIR;

LEARNING ALGORITHMS;

EID: 77956441214     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CSO.2010.128     Document Type: Conference Paper

References (9)

1. J. B. Tenenbaum, V. de Silva, and J. C. Langford, "A Global Geometric Framework for Nonlinear Dimensionality Reduction", Science, vol.290, pp. 2319-2323, December 2000.
2. S. T. Roweis and L. K. Saul, "Nonlinear Dimensionality Reduction by Locally Linear Embedding", Science, vol.290, pp. 2323-2326, December 2000.
3. M. Balasubramanian, E. L. Shwartz, J. B. Tenenbaum, V. de Silva, and J. C. Langford, "The Isomap Algorithm and Topological Stability", Science, vol.295, pp. 7, January 2002.
4. C. Shao, H. Huang, and C. Wan, "Selection of the Suitable Neighborhood Size for the ISOMAP Algorithm", Proceedings of International Joint Conference on Neural Networks (IJCNN 2007), IEEE press, Aug. 2007, pp. 300-305.
5. O. Kouropteva, O. Okun, and M. Pietikäinen, "Selection of the Optimal Parameter Value for the Locally Linear Embedding Algorithm", Proceedings of the 1st International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2002), IEEE press, Nov. 2002, pp. 359-363.
6. A. Saxena, A. Gupta, and A. Mukerjee, "Non-linear Dimensionality Reduction by Locally Linear Isomaps", Proceedings of the 11th International Conference on Neural Information Processing (ICONIP 2004), Springer-Verlag, Nov. 2004, pp. 1038-1043.
7. J. A. Lee, A. Lendasse, and M. Verleysen, "Nonlinear Projection with Curvilinear Distances: Isomap versus Curvilinear Distance Analysis", Neurocomputing, Vol.57, pp. 49-76, March 2004.
8. C. Varini, A. Degenhard, T. W. Nattkemper, "ISOLLE: LLE with Geodesic Distance", Neurocomputing, Vol.69, pp. 1768-1771, August 2006.
9. D. Pelleg, A. Moore, "X-means: Extending K-means with Efficient Estimation of the Number of Clusters", Proceedings of the 17th International Conference on Machine Learning, Morgan Kaufmann, Jul. 2000, pp.727-734.