Nonlinear manifold clustering by dimensionality

Proceedings - International Conference on Pattern Recognition

Volumn 1, Issue , 2006, Pages 920-924

  Wenbo, Cao ^a   Haralick, Robert ^a  

^a CITY UNIVERSITY OF NEW YORK   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STRUCTURES; DATABASE SYSTEMS; FEATURE EXTRACTION; GRAPH THEORY; MATHEMATICAL MODELS; NONLINEAR SYSTEMS;

DATA CLUSTERING; NONLINEAR MANIFOLD CLUSTERING ALGORITHM; NONLINEAR STRUCTURE;

CLUSTERING ALGORITHMS;

EID: 34047205956     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2006.865     Document Type: Conference Paper

References (13)

1. C. Aggarwal, C. Procopiuc, J. Wolf, P. Yu, and J. Park. Fast algorithms for projected clustering. In Proceedings of the 1999 ACM SIGMOD international conference on management of data, pages 61-72, 1999.
2. C. Aggarwal and P. Yu. Finding generalized projected clusters in high dimensional spaces. In Proceedings of the 2000 ACM SIGMOD international conference on management of data, pages 70-81, 2000.
3. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In Proceedings of the ACM SIGMOD international conference on management of data, pages 94-105, 1998.
4. M. Belkin and P. Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. Neural Information Processing Systems, 14:585-591, 2001.
5. C. Blake and C. Merz. Uci repositroy of machine learning databases. http://www.ics.uci.edu/mlearn/MLRepository.html.
6. R. Duda, P. Hart, and D. Stork. Pattern Classification. Wiley Interscience, 2nd edition, 2001.
7. R. Haralick and R. Harpaz. Linear manifold clustering. In 4th International Conference on Machine Learning and Data Mining in Pattern Recognition, pages 132-141, 2005.
8. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer Verlag, New York, 2001.
9. K. Müler, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. An introduction to kernel-based learning algorithms. IEEE Transaction on Neural Networks, 12(2):181-202, 2001.
10. J. Munkres. Topology. Prentice Hall, 1999.
11. A. Ng, M. Jordan, and Y. Weiss. On spectral clustering: analysis and an algorithm. Advances in Neural Information Processing Systems, 14:849-856, 2002.
12. J. Shi and J. Malik. Normalized cuts and image segmentation. In Proc. IEEE Conference of Computer Vision and Pattern Recognition, pages 731-737, 1997.
13. Y. Weiss. Segmentation using eigenvectors: a unifying view. In International Conference on Computer Vision, 1999.