Have a personal or library account? Click to login
Constrained spectral clustering via multi–layer graph embeddings on a grassmann manifold Cover

Constrained spectral clustering via multi–layer graph embeddings on a grassmann manifold

Open Access
|Mar 2019

References

  1. Choromanska, A., Jebara, T., Kim, H., Mohan, M. and Monteleoni, C. (2013). Fast spectral clustering via the Nyström method, International Conference on Algorithmic Learning Theory, Singapore, Republic of Singapore, pp. 367–381.10.1007/978-3-642-40935-6_26
  2. Coleman, T., Saunderson, J. and Wirth, A. (2008). Spectral clustering with inconsistent advice, 25th International Conference on Machine Learning, Helsinki, Finland, pp. 152–159.10.1145/1390156.1390176
  3. De Bie, T., Suykens, J. and De Moor, B. (2004). Learning from general label constraints, Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), Lisbon, Portugal, pp. 671–679.10.1007/978-3-540-27868-9_73
  4. Dong, X., Frossard, P., Vandergheynst, P. and Nefedov, N. (2014). Clustering on multi-layer graphs via subspace analysis on Grassmann manifolds, IEEE Transactions on Signal Processing62(4): 905–918.10.1109/TSP.2013.2295553
  5. Fowlkes, C., Belongie, S., Chung, F. and Malik, J. (2004). Spectral grouping using the Nystrom method, IEEE Transactions on Pattern Analysis and Machine Intelligence26(2): 214–225.10.1109/TPAMI.2004.126218515376896
  6. Golub, G.H. and Van Loan, C.F. (1996). Matrix Computations, Johns Hopkins University Press, Baltimore, MD, pp. 374–426.
  7. Hamm, J. and Lee, D.D. (2008). Grassmann discriminant analysis: A unifying view on subspace-based learning, Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, pp. 376–383.10.1145/1390156.1390204
  8. Hamm, J. and Lee, D.D. (2009). Extended Grassmann kernels for subspace-based learning, in D. Koller et al. (Eds.), Advances in Neural Information Processing Systems 21, Curran Associates, Inc., Vancouver, pp. 601–608.
  9. Harandi, M.T., Sanderson, C., Shirazi, S. and Lovell, B.C. (2011). Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA, pp. 2705–2712.10.1109/CVPR.2011.5995564
  10. Kamvar, S.D., Klein, D. and Manning, C.D. (2003). Spectral learning, 18th International Joint Conference on Artificial Intelligence, Acapulco, Mexico, pp. 561–566.
  11. Kumar, S., Mohri, M. and Talwalkar, A. (2009). Sampling techniques for the Nyström method, 12th International Conference on Artificial Intelligence and Statistics, Barcelona, Spain, pp. 304–311.
  12. Li, J., Xia, Y., Shan, Z. and Liu, Y. (2015). Scalable constrained spectral clustering, IEEE Transactions on Knowledge and Data Engineering27(2): 589–593.10.1109/TKDE.2014.2356471
  13. Li, M., Lian, X.-C., Kwok, J.T. and Lu, B.-L. (2011). Time and space efficient spectral clustering via column sampling, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA, pp. 2297–2304.10.1109/CVPR.2011.5995425
  14. Li, Z., Liu, J. and Tang, X. (2009). Constrained clustering via spectral regularization, IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, pp. 421–428.10.1109/CVPR.2009.5206852
  15. Lichman, M. (2013). UCI machine learning repository, University of California Irvine, Irvine, CA, http://archive.ics.uci.edu/ml.
  16. Lu, Z. and Carreira-Perpinan, M.A. (2008). Constrained spectral clustering through affinity propagation, IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, pp. 1–8.
  17. Manning, C.D., Raghavan, P. and Schütze, H. (2008). Introduction to Information Retrieval, Cambridge University Press, New York, NY.10.1017/CBO9780511809071
  18. Ng, A.Y., Jordan, M.I. and Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm, Advances in Neural Information Processing Systems2(14): 849–856.
  19. Turk, M. and Pentland, A. (1991). Eigenfaces for recognition, Journal of Cognitive Neuroscience3(1): 71–86.10.1162/jocn.1991.3.1.7123964806
  20. Von Luxburg, U. (2007). A tutorial on spectral clustering, Statistics and Computing17(4): 395–416.10.1007/s11222-007-9033-z
  21. Wang, X. (2014). On constrained spectral clustering and its applications (Matlab code), https://sites.google.com/site/gnaixgnaw/home.
  22. Wang, X., Qian, B. and Davidson, I. (2014). On constrained spectral clustering and its applications, Data Mining and Knowledge Discovery28(1): 1–30.10.1007/s10618-012-0291-9
  23. White, S. and Smyth, P. (2005). A spectral clustering approach to finding communities in graphs, Proceedings of the 2005 SIAM International Conference on Data Mining, Newport Beach, CA, USA, pp. 274–285.10.1137/1.9781611972757.25
  24. Xu, Q., des Jardins, M. and Wagstaff, K. (2005). Constrained spectral clustering under a local proximity structure assumption, 18th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS-05), Clearwater Beach, FL, USA, pp. 866–867.
DOI: https://doi.org/10.2478/amcs-2019-0010 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 125 - 137
Submitted on: Feb 2, 2018
Accepted on: Oct 16, 2018
Published on: Mar 29, 2019
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2019 Aleksandar Trokicić, Branimir Todorović, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.