1. bookVolume 29 (2019): Issue 1 (March 2019)
    Exploring Complex and Big Data (special section, pp. 7-91), Johann Gamper, Robert Wrembel (Eds.)
Journal Details
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Journal
eISSN
2083-8492
First Published
05 Apr 2007
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4 times per year
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English
Open Access

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

Published Online: 29 Mar 2019
Volume & Issue: Volume 29 (2019) - Issue 1 (March 2019) - Exploring Complex and Big Data (special section, pp. 7-91), Johann Gamper, Robert Wrembel (Eds.)
Page range: 125 - 137
Received: 02 Feb 2018
Accepted: 16 Oct 2018
Journal Details
License
Format
Journal
eISSN
2083-8492
First Published
05 Apr 2007
Publication timeframe
4 times per year
Languages
English

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.Search in Google Scholar

Coleman, T., Saunderson, J. and Wirth, A. (2008). Spectral clustering with inconsistent advice, 25th International Conference on Machine Learning, Helsinki, Finland, pp. 152–159.Search in Google Scholar

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.Search in Google Scholar

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.2013.2295553Search in Google Scholar

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.2004.126218515376896Search in Google Scholar

Golub, G.H. and Van Loan, C.F. (1996). Matrix Computations, Johns Hopkins University Press, Baltimore, MD, pp. 374–426.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

Kamvar, S.D., Klein, D. and Manning, C.D. (2003). Spectral learning, 18th International Joint Conference on Artificial Intelligence, Acapulco, Mexico, pp. 561–566.Search in Google Scholar

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.Search in Google Scholar

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.2014.2356471Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

Lichman, M. (2013). UCI machine learning repository, University of California Irvine, Irvine, CA, http://archive.ics.uci.edu/ml.Search in Google Scholar

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.Search in Google Scholar

Manning, C.D., Raghavan, P. and Schütze, H. (2008). Introduction to Information Retrieval, Cambridge University Press, New York, NY.10.1017/CBO9780511809071Search in Google Scholar

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.Search in Google Scholar

Turk, M. and Pentland, A. (1991). Eigenfaces for recognition, Journal of Cognitive Neuroscience3(1): 71–86.10.1162/jocn.1991.3.1.7123964806Search in Google Scholar

Von Luxburg, U. (2007). A tutorial on spectral clustering, Statistics and Computing17(4): 395–416.10.1007/s11222-007-9033-zSearch in Google Scholar

Wang, X. (2014). On constrained spectral clustering and its applications (Matlab code), https://sites.google.com/site/gnaixgnaw/home.Search in Google Scholar

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/s10618-012-0291-9Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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