Capped ℓp-Norm graph embedding for photo clustering

MM 2016 - Proceedings of the 2016 ACM Multimedia Conference

Volumn , Issue , 2016, Pages 431-435

  Tang, Mengfan ^a   Nie, Feiping ^b   Jain, Ramesh ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

Capped p norm; Photo clustering; Unsupervised learning; Yahoo Flickr Creative Commons data

Indexed keywords

CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; ITERATIVE METHODS; OPTIMIZATION; STATISTICS; UNSUPERVISED LEARNING;

CLUSTERING PROBLEMS; CREATIVE COMMONS; GRAPH-BASED LEARNING; GRAPH-BASED MODELING; ITERATIVE ALGORITHM; OBJECTIVE FUNCTIONS; PHOTO CLUSTERING; SITUATION RECOGNITION;

GRAPHIC METHODS;

EID: 84994560116     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2964284.2967257     Document Type: Conference Paper

References (23)

1. J. Bernd, D. Borth, C. Carrano, J. Choi, B. Elizalde, G. Friedland, L. Gottlieb, K. Ni, R. Pearce, D. Poland, et al. Kickstarting the commons: The yfcc100m and the yli corpora. In Proceedings of the 2015 Workshop on Community-Organized Multimodal Mining: Opportunities for Novel Solutions, pages 1-6. ACM, 2015.
2. P. K. Chan, M. D. Schlag, and J. Y. Zien. Spectral k-way ratio-cut partitioning and clustering. Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on, 13(9):1088-1096, 1994.
3. O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi-Supervised Learning. MIT Press, Cambridge, MA, 2006.
4. P. Gong, J. Ye, and C.-S. Zhang. Multi-stage multi-task feature learning. In Advances in Neural Information Processing Systems, pages 1988-1996, 2012.
5. N. Hochman and L. Manovich. Zooming into an instagram city: Reading the local through social media. First Monday, 18(7), 2013.
6. R. Jain. Let's weave the visual web. MultiMedia, IEEE, 22(3):66-72, 2015.
7. X. Jin, A. Gallagher, L. Cao, J. Luo, and J. Han. The wisdom of social multimedia: using ickr for prediction and forecast. In Proceedings of the international conference on Multimedia, pages 1235-1244. ACM, 2010.
8. Y. LeCun, J. S. Denker, S. A. Solla, R. E. Howard, and L. D. Jackel. Optimal brain damage. In NIPs, volume 89, 1989.
9. T. M. Mitchell. Machine learning. Computer Science Series (McGraw-Hill, Burr Ridge, 1997) MATH, 1997.
10. S. A. Nene, S. K. Nayar, H. Murase, et al. Columbia object image library (coil-20). Technical report, technical report CUCS-005-96, 1996.
11. F. Nie, H. Huang, X. Cai, and C. H. Ding. Efficient and robust feature selection via joint l2,1-norms minimization. In Advances in neural information processing systems, pages 1813-1821, 2010.
12. F. Nie, H. Wang, H. Huang, and C. Ding. Unsupervised and semi-supervised learning via l1-norm graph. In ICCV, pages 2268-2273. IEEE, 2011.
13. F. Nie, X. Wang, M. I. Jordan, and H. Huang. The constrained laplacian rank algorithm for graph-based clustering. In AAAI, 2016.
14. F. Nie, J. Yuan, and H. Huang. Optimal mean robust principal component analysis. In ICML, pages 1062-1070, 2014.
15. S. Pongpaichet, M. Tang, L. Jalali, and R. Jain. Using photos as micro-reports of events. In Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, pages 87-94. ACM, 2016.
16. J. Shi and J. Malik. Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(8):888-905, 2000.
17. M. Tang, P. Agrawal, F. Nie, S. Pongpaichet, and R. Jain. A graph based multimodal geospatial interpolation framework. In 2016 IEEE International Conference on Multimedia and Expo (ICME), pages 1-6. IEEE, 2016.
18. B. Thomee, B. Elizalde, D. A. Shamma, K. Ni, G. Friedland, D. Poland, D. Borth, and L.-J. Li. Yfcc100m: The new data in multimedia research. Communications of the ACM, 59(2):64-73, 2016.
19. Y. Wang and M. S. Kankanhalli. Tweeting cameras for event detection. In Proceedings of the 24th International Conference on World Wide Web, pages 1231-1241. International World Wide Web Conferences Steering Committee, 2015.
20. H. Xu, C. Caramanis, and S. Mannor. Sparse algorithms are not stable: A no-free-lunch theorem. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(1):187-193, 2012.
21. M. Yang, L. Zhang, J. Yang, and D. Zhang. Robust sparse coding for face recognition. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 625-632. IEEE, 2011.
22. G. Ye, M. Tang, J.-F. Cai, Q. Nie, and X. Xie. Low-rank regularization for learning gene expression programs. PloS one, 8(12):e82146, 2013.
23. T. Zhang. Analysis of multi-stage convex relaxation for sparse regularization. The Journal of Machine Learning Research, 11:1081-1107, 2010.