Efficient out-of-sample extension of dominant-set clusters

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages

  Pavan, Massimiliano ^a   Pelillo, Marcello ^a  

^a CA' FOSCARI UNIVERSITY OF VENICE   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

GRAPH THEORY;

CLUSTERING PROCESS; COMPUTATIONAL BURDEN; EDGE-WEIGHTED GRAPH; GROUPING PROBLEM; NUMERICAL EXPERIMENTS; OUT-OF-SAMPLE EXTENSION; QUADRATIC OPTIMIZATION; VERY LARGE DATUM;

CLUSTERING ALGORITHMS;

EID: 84899027792     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Y. Bengio, J.-F. Paiement, P. Vincent, O. Delalleau, N. Le Roux, and M. Ouimet. Out-of-sample extensions for LLE, Isomap, MDS, eigenmaps, and spectral clustering. In: S. Thrun, L. Saul, and B.Scholkopf (Eds.), Advances in Neural Information Processing Systems 16, MIT Press, Cambridge, MA, 2004.
2. C. Fowlkes, S. Belongie, F. Chun, and J. Malik. Spectral grouping using the Nystrom method. IEEE Trans. Pattern Anal. Machine Intell. 26:214-225, 2004.
3. T. Hofmann and J. M. Buhmann. Pairwise data clustering by deterministic annealing. IEEE Trans. Pattern Anal. Machine Intell. 19:1-14, 1997.
4. D. Luenberger. Linear and Nonlinear Programming. Addison-Wesley, Reading, MA, 1984.
5. T. S. Motzkin and E. G. Straus. Maxima for graphs and a new proof of a theorem of Turan. Canad. J. Math. 17:533-540, 1965.
6. A. Y. Ng, M. I. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In: T. G. Dietterich, S. Becker, and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14, MIT Press, Cambridge, MA, pp. 849-856, 2002.
7. M. Pavan and M. Pelillo. A new graph-theoretic approach to clustering and segmentation. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 145-152, 2003.
8. M. Pavan, M. Pelillo. Unsupervised texture segmentation by dominant sets and game dynamics. In Proc. 12th Int. Conf. on Image Analysis and Processing, pp. 302-307, 2003.
9. M. Pavan and M. Pelillo. Dominant sets and hierarchical clustering. In Proc. 9th Int. Conf. on Computer Vision, pp. 362-369, 2003.
10. P. Perona and W. Freeman. A factorization approach to grouping. In: H. Burkhardt and B. Neumann (Eds.), Computer Vision-ECCV'98, pp. 655-670. Springer, Berlin, 1998.
11. V. Roth, J. Laub, M. Kawanabe, and J. M. Buhmann. Optimal cluster preserving embedding of nonmetric proximity data. IEEE Trans. Pattern Anal. Machine Intell. 25:1540-1551, 2003.
12. S. Sarkar and K. Boyer. Quantitative measures of change based on feature organization: Eigenvalues and eigenvectors. Computer Vision and Image Understanding 71:110-136, 1998.
13. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Machine Intell. 22:888-905, 2000.
14. J. W. Weibull. Evolutionary Game Theory. MIT Press, Cambridge, MA, 1995.
15. Y. Weiss. Segmentation using eigenvectors: A unifying view. In Proc. 7th Int. Conf. on Computer Vision, pp. 975-982, 1999.