The constrained laplacian rank algorithm for graph-based clustering

30th AAAI Conference on Artificial Intelligence, AAAI 2016

Volumn , Issue , 2016, Pages 1969-1976

  Nie, Feiping ^a   Wang, Xiaoqian ^a   Jordan, Michael I ^b   Huang, Heng ^a  

^a University of Texas at Arlington   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; DATA HANDLING; GRAPHIC METHODS; LAPLACE TRANSFORMS; OPTIMIZATION;

BENCHMARK DATASETS; CLUSTERING PROCEDURE; GRAPH-BASED CLUSTERING; NUMBER OF CLUSTERS; OPTIMIZATION ALGORITHMS; POST PROCESSING; RANK ALGORITHMS; SYNTHETIC DATASETS;

CLUSTERING ALGORITHMS;

EID: 84994562309     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Asuncion, A., and Newman, D. 2007. UCI Machine Learning Repository.
2. Chung, F. R. K. 1997. Spectral Graph Theory. CBMS Regional Conference Series in Mathematics, No. 92, American Mathematical Society.
3. Ding, C. H. Q., and He, X. 2005. On the equivalence of nonnegative matrix factorization and spectral clustering. In SDM.
4. Fan, K. 1949. On a theorem of Weyl concerning eigenvalues of linear transformations. 35(11):652-655.
5. Graham, D. B., and Allinson, N. M. 1998. Characterizing virtual eigensignatures for general-purpose face recognition. NATO ASI Series F, Computer and Systems Sciences 163:446-456.
6. Hagen, L. W., and Kahng, A. B. 1992. New spectral methods for ratio cut partitioning and clustering. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 11(9):1074-1085.
7. Huang, J.; Nie, F.; and Huang, H. 2015. A new simplex sparse learning model to measure data similarity for clustering. In Proceedings of the 24th International Conference on Artificial Intelligence, 3569-3575.
8. Li, T., and Ding, C. H. Q. 2006. The relationships among various nonnegative matrix factorization methods for clustering. In ICDM, 362-371.
9. Martinez, A. 1998. The AR face database. CVC Technical Report 24.
10. Mohar, B. 1991. The Laplacian spectrum of graphs. In Graph Theory, Combinatorics, and Applications, 871-898. Wiley.
11. Nene, S. A.; Nayar, S. K.; and Murase, H. 1996a. Columbia object image library (COIL-100), Technical Report CUCS- 006-96.
12. Nene, S. A.; Nayar, S. K.; and Murase, H. 1996b. Columbia object image library (COIL-20), Technical Report CUCS- 005-96.
13. Ng, A. Y.; Jordan, M. I.; and Weiss, Y. 2001. On spectral clustering: Analysis and an algorithm. In NIPS, 849-856.
14. Nie, F.; Huang, H.; Cai, X.; and Ding, C. 2010. Efficient and robust feature selection via joint -2, 1-norms minimization. In NIPS.
15. Nie, F.; Wang, X.; and Huang, H. 2014. Clustering and projected clustering with adaptive neighbors. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 977-986.
16. Roweis, S. T., and Saul, L. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323- 2326.
17. Shi, J., and Malik, J. 2000. Normalized cuts and image segmentation. IEEE PAMI 22(8):888-905.
18. XM2VTS. http://www.ee.surrey.ac.uk/cvssp/xm2vtsdb/. Zelnik-Manor, L., and Perona, P. 2004. Self-Tuning spectral clustering. In NIPS.