Graph clustering with missing data: Convex algorithms and analysis

Advances in Neural Information Processing Systems

Volumn 4, Issue January, 2014, Pages 2996-3004

  Vinayak, Ramya Korlakai ^a   Oymak, Samet ^a   Hassibi, Babak ^a  

^a California Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); CONVEX OPTIMIZATION; DATA MINING; IMAGE CLASSIFICATION; INFORMATION SCIENCE; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

AMAZON MECHANICAL TURKS; EXTENSIVE SIMULATIONS; LOW-RANK MATRIX RECOVERIES; NUCLEAR NORM MINIMIZATIONS; OPTIMIZATION APPROACH; REGULARIZATION PARAMETERS; STOCHASTIC BLOCK MODELS; UNWEIGHTED GRAPHS;

GRAPH THEORY;

EID: 84937908958     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: A review. ACM Comput. Surv., 31(3): 264-323, September 1999.
2. M. Ester, H.-P. Kriegel, and X. Xu. A database interface for clustering in large spatial databases. In Proceedings of the 1st international conference on Knowledge Discovery and Data mining (KDD'95), pages 94-99. AAAI Press, August 1995.
3. Xiaowei Xu, Jochen Jäger, and Hans-Peter Kriegel. A fast parallel clustering algorithm for large spatial databases. Data Min. Knowl. Discov., 3(3): 263-290, September 1999.
4. Nina Mishra, Robert Schreiber, Isabelle Stanton, and Robert Tarjan. Clustering Social Networks. In Anthony Bonato and Fan R. K. Chung, editors, Algorithms and Models for the Web-Graph, volume 4863 of Lecture Notes in Computer Science, chapter 5, pages 56-67. Springer Berlin Heidelberg, Berlin, Heidelberg, 2007.
5. Pedro Domingos and Matt Richardson. Mining the network value of customers. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '01, pages 57-66, New York, NY, USA, 2001. ACM.
6. Santo Fortunato. Community detection in graphs. Physics Reports, 486(3-5): 75-174, 2010.
7. Ying Xu, Victor Olman, and Dong Xu. Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees. Bioinformatics, 18(4): 536-545, 2002.
8. Qiaofeng Yang and Stefano Lonardi. A parallel algorithm for clustering protein-protein interaction networks. In CSB Workshops, pages 174-177. IEEE Computer Society, 2005.
9. Satu Elisa Schaeffer. Graph clustering. Computer Science Review, 1(1): 27-64, 2007.
10. Paul W. Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt. Stochastic blockmodels: First steps. Social Networks, 5(2): 109-137, 1983.
11. Anne Condon and Richard M. Karp. Algorithms for graph partitioning on the planted partition model. Random Struct. Algorithms, 18(2): 116-140, 2001.
12. Huan Xu, Constantine Caramanis, and Sujay Sanghavi. Robust pca via outlier pursuit. In John D. Lafferty, Christopher K. I. Williams, John Shawe-Taylor, Richard S. Zemel, and Aron Culotta, editors, NIPS, pages 2496-2504. Curran Associates, Inc., 2010.
13. Ali Jalali, Yudong Chen, Sujay Sanghavi, and Huan Xu. Clustering partially observed graphs via convex optimization. In Lise Getoor and Tobias Scheffer, editors, Proceedings of the 28th International Conference on Machine Learning (ICML-11), ICML'11, pages 1001-1008, New York, NY, USA, June 2011. ACM.
14. Brendan P. W. Ames and Stephen A. Vavasis. Convex optimization for the planted k-disjoint-clique problem. Math. Program., 143(1-2): 299-337, 2014.
15. Brendan P. W. Ames and Stephen A. Vavasis. Nuclear norm minimization for the planted clique and biclique problems. Math. Program., 129(1): 69-89, September 2011.
16. S. Oymak and B. Hassibi. Finding Dense Clusters via "Low Rank + Sparse" Decomposition. arXiv: 1104.5186, April 2011.
17. Yudong Chen, Sujay Sanghavi, and Huan Xu. Clustering sparse graphs. In Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, Lon Bottou, and Kilian Q. Weinberger, editors, NIPS, pages 2213-2221, 2012.
18. Yudong Chen, Ali Jalali, Sujay Sanghavi, and Constantine Caramanis. Low-rank matrix recovery from errors and erasures. IEEE Transactions on Information Theory, 59(7): 4324-4337, 2013.
19. Brendan P. W. Ames. Robust convex relaxation for the planted clique and densest k-subgraph problems. 2013.
20. Nir Ailon, Yudong Chen, and Huan Xu. Breaking the small cluster barrier of graph clustering. CoRR, abs/1302.4549, 2013.
21. Ramya Korlakai Vinayak, Samet Oymak, and Babak Hassibi. Sharp performance bounds for graph clustering via convex optimizations. In Proceedings of the 39th International Conference on Acoustics, Speech and Signal Processing, ICASSP '14, 2014.
22. Emmanuel J. Candes and Justin Romberg. Quantitative robust uncertainty principles and optimally sparse decompositions. Found. Comput. Math., 6(2): 227-254, April 2006.
23. Emmanuel J. Candes and Benjamin Recht. Exact matrix completion via convex optimization. Found. Comput. Math., 9(6): 717-772, December 2009.
24. Emmanuel J. Candès, Xiaodong Li, Yi Ma, and John Wright. Robust principal component analysis? J. ACM, 58(3): 11:1-11:37, June 2011.
25. Venkat Chandrasekaran, Sujay Sanghavi, Pablo A. Parrilo, and Alan S. Willsky. Rank-sparsity incoherence for matrix decomposition. SIAM Journal on Optimization, 21(2): 572-596, 2011.
26. Venkat Chandrasekaran, Pablo A. Parrilo, and Alan S. Willsky. Rejoinder: Latent variable graphical model selection via convex optimization. CoRR, abs/1211.0835, 2012.
27. Frank McSherry. Spectral partitioning of random graphs. In FOCS, pages 529-537. IEEE Computer Society, 2001.
28. Zhouchen Lin, Minming Chen, and Yi Ma. The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices. Mathematical Programming, 2010.
29. Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei. Novel dataset for finegrained image categorization. In First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, June 2011.
30. Michael Buhrmester, Tracy Kwang, and Samuel D. Gosling. Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1): 3-5, January 2011.