Multi-exemplar affinity propagation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 35, Issue 9, 2013, Pages 2223-2237

  Wang, Chang Dong ^a   Lai, Jian Huang ^a   Suen, Ching Y ^b   Zhu, Jun Yong ^a  

^a SUN YAT SEN UNIVERSITY   (China)

^b Civil and Environmental Engineering   (Canada)

Author keywords

affinity propagation; Clustering; factor graph; max product belief propagation; multi exemplar

Indexed keywords

AFFINITY PROPAGATION; BELIEF PROPAGATION; CLUSTERING; FACTOR GRAPHS; MULTI-EXEMPLAR;

CHARACTER RECOGNITION; DIGITAL STORAGE;

CLUSTERING ALGORITHMS;

ALGORITHM; ANATOMY AND HISTOLOGY; AUTOMATED PATTERN RECOGNITION; BIOMETRY; CLUSTER ANALYSIS; FACE; FACIAL EXPRESSION; FACTUAL DATABASE; FEMALE; HANDWRITING; HUMAN; IMAGE PROCESSING; PROCEDURES; ARTICLE; HISTOLOGY; METHODOLOGY;

ALGORITHMS; BIOMETRIC IDENTIFICATION; CLUSTER ANALYSIS; DATABASES, FACTUAL; FACE; FACIAL EXPRESSION; FEMALE; HANDWRITING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED;

EID: 84880876970     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2013.28     Document Type: Article

References (48)

1. B.J. Frey and D. Dueck, "Clustering by Passing Messages between Data Points," Science, vol. 315, pp. 972-976, http://www.psi. toronto.edu/index.php?q=affinity%20propagation, 2007. (Pubitemid 46281181)
2. F.R. Kschischang, B.J. Frey, and H.-A. Loeliger, "Factor Graphs and the Sum-Product Algorithm," IEEE Trans. Information Theory, vol. 47, no. 2, pp. 498-519, Feb. 2001. (Pubitemid 32318087)
3. Y. Weiss and W.T. Freeman, "On the Optimality of Solutions of the Max-Product Belief-Propagation Algorithm in Arbitrary Graphs," IEEE Trans. Information Theory, vol. 47, no. 2, pp. 736-744, Feb. 2001. (Pubitemid 32318100)
4. J. MacQueen, "Some Methods for Classification and Analysis of Multivariate Observations," Proc. 15th Berkeley Symp. Math. Statistics and Probability, vol. 1, pp. 281-297, 1967.
5. A.K. Jain, "Data Clustering: 50 Years Beyond K-Means," Pattern Recognition Letters, vol. 31, pp. 651-666, 2010.
6. D. Dueck, B.J. Frey, N. Jojic, V. Jojic, G. Giaever, A. Emili, G. Musso, and R. Hegele, "Constructing Treatment Portfolios Using Affinity Propagation," Proc. 12th Ann. Int'l Conf. Research in Computational Molecular Biology, pp. 360-371, 2008.
7. T.-H. Huang, K.-Y. Cheng, and Y.-Y. Chuang, "A Collaborative Benchmark for Region of Interest Detection Algorithms," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 296-303, 2009.
8. R. Verma and P. Wang, "On Detecting Subtle Pathology via Tissue Clustering of Multi-Parametric Data Using Affinity Propagation," Proc. 11th IEEE Int'l Conf. Computer Vision, pp. 1-8, 2007.
9. D. Dueck and B.J. Frey, "Non-Metric Affinity Propagation for Unsupervised Image Categorization," Proc. 11th IEEE Int'l Conf. Computer Vision, pp. 1-8, 2007.
10. Z.-Q. Zhao, J. Gao, H. Glotin, and X. Wu, "A Matrix Modular Neural Network Based on Task Decomposition with Subspace Division by Adaptive Affinity Propagation Clustering," Applied Math. Modelling, vol. 34, pp. 3884-3895, 2010.
11. M.L. Sumedha and M. Weigt, "Unsupervised and Semi-Supervised Clustering by Message Passing: Soft-Constraint Affinity Propagation," European Physical J. B, vol. 66, pp. 125-135, 2008.
12. D. Tarlow, R.S. Zemel, and B.J. Frey, "Flexible Priors for Exemplar-Based Clustering," Proc. 24th Conf. Uncertainty in Artificial Intelligence, pp. 537-545, 2008.
13. X. Zhang, C. Furtlehner, and M. Sebag, "Data Streaming with Affinity Propagation," Proc. European Conf. Machine Learning and Knowledge Discovery in Databases, pp. 628-643, 2008.
14. I.E. Givoni and B.J. Frey, "Semi-Supervised Affinity Propagation with Instance-Level Constraints," Proc. Conf. Artificial Intelligence and Statistics, pp. 161-168, 2009.
15. J. Xiao, J. Wang, P. Tan, and L. Quan, "Joint Affinity Propagation for Multiple View Segmentation," Proc. IEEE Int'l Conf. Computer Vision, pp. 1-7, 2007.
16. I.E. Givoni, C. Chung, and B.J. Frey, "Hierarchical Affinity Propagation," Proc. 24th Conf. Uncertainty in Artificial Intelligence, pp. 238-246, 2011.
17. L. Fei-Fei and P. Perona, "A Bayesian Hierarchical Model for Learning Natural Scene Categories," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 524-531, 2005.
18. M. Zhu and A.M. Martinez, "Subclass Discriminant Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 8, pp. 1274-1286, Aug. 2006. (Pubitemid 46405025)
19. H.I. Avi-Itzhak, J.A.V. Mieghem, and L. Rub, "Multiple Subclass Pattern Recognition: A Maximin Correlation Approach," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 4, pp. 418-431, Apr. 1995.
20. A.F.R. Rahman and M. Fairhurst, "Multi-Prototype Classification: Improved Modelling of the Variability of Handwritten Data Using Statistical Clustering Algorithms," Electronics Letters, vol. 33, pp. 1208-1210, 1997. (Pubitemid 127536229)
21. B. Schölkopf, A. Smola, and K.-R. Mü ller, "Nonlinear Component Analysis as a Kernel Eigenvalue Problem," Neural Computation, vol. 10, pp. 1299-1319, 1998.
22. J. Shi and J. Malik, "Normalized Cuts and Image Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000.
23. C.-D. Wang, J.-H. Lai, and J.-Y. Zhu, "A Conscience On-Line Learning Approach for Kernel-Based Clustering," Proc. 10th Int'l Conf. Data Mining, pp. 531-540, 2010.
24. I.S. Dhillon, Y. Guan, and B. Kulis, "Weighted Graph Cuts Without Eigenvectors: A Multilevel Approach," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 11, pp. 1944-1957, Nov. 2007. (Pubitemid 47430448)
25. S.K. Zhou and R. Chellappa, "Multiple-Exemplar Discriminant Analysis for Face Recognition," Proc. 17th Int'l Conf. Pattern Recognition, pp. 191-194, 2004.
26. J. Reisinger and R.J. Mooney, "Multi-Prototype Vector-Space Models of Word Meaning," Proc. Ann. Conf. North Am. Chapter of the Assoc. for Computational Linguistics, pp. 109-117, 2010.
27. Q. Zhu, Y. Cai, and L. Liu, "A Multiple Hyper-Ellipsoidal Subclass Model for an Evolutionary Classifier," Pattern Recognition, vol. 34, pp. 547-560, 2001. (Pubitemid 32133391)
28. F. Aiolli and A. Sperduti, "Multiclass Classification with Multi-Prototype Support Vector Machines," J. Machine Learning Research, vol. 6, pp. 817-850, 2005.
29. S. Guha, R. Rastogi, and K. Shim, "CURE: An Efficient Clustering Algorithm for Large Databases," Information Systems, vol. 26, no. 1, pp. 35-58, 2001. (Pubitemid 32297227)
30. M. Liu, X. Jiang, and A.C. Kot, "A Multi-Prototype Clustering Algorithm," Pattern Recognition, vol. 42, pp. 689-698, 2009.
31. T. Luo, C. Zhong, H. Li, and X. Sun, "A Multi-Prototype Clustering Algorithm Based on Minimum Spanning Tree," Proc. Seventh Int'l Conf. Fuzzy Systems and Knowledge Discovery, pp. 1602-1607, 2010.
32. D. Dueck, "Affinity Propagation: Clustering Data by Passing Messages," PhD dissertation, Univ. of Toronto, 2009.
33. I.E. Givoni and B.J. Frey, "A Binary Variable Model for Affinity Propagation," Neural Computation, vol. 21, no. 6, pp. 1589-1600, June 2009.
34. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge Univ. Press, 2004.
35. L. Xu, J. Neufeld, B. Larson, and D. Schuurmans, "Maximum Margin Clustering," Proc. Conf. Neural Information Processing Systems, 2004.
36. M.J. Lyons, S. Akamatsu, M. Kamachi, and J. Gyoba, "Coding Facial Expressions with Gabor Wavelets," Proc. Third IEEE Int'l Conf. Automatic Face and Gesture Recognition, pp. 200-205, 1998.
37. L. Fei-Fei, R. Fergus, and P. Perona, "Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories," Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop, pp. 178-188, 2004.
38. D. You, O.C. Hamsici, and A.M. Martinez, "Kernel Optimization in Discriminant Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 3, pp. 631-638, Mar. 2011.
39. K. Grauman and T. Darrell, "Unsupervised Learning of Categories from Sets of Partially Matching Image Features," Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop, pp. 2596-2603, 2006.
40. K. Mikolajczyk, B. Leibe, and B. Schiele, "Multiple Object Class Detection with a Generative Model," Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop, pp. 26-36, 2006.
41. D.G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Int'l J. Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
42. A. Strehl and J. Ghosh, "Cluster Ensembles-A Knowledge Reuse Framework for Combining Multiple Partitions," J. Machine Learning Research, vol. 3, pp. 583-617, 2002.
43. A. Strehl, J. Ghosh, and R.J. Mooney, "Impact of Similarity Measures on Web-Page Clustering," Proc. AAAI Workshop AI for Web Search, pp. 58-64, 2000.
44. L. Hubert and P. Arabie, "Comparing Partitions," J. Classification, vol. 2, pp. 193-218, 1985.
45. M. Meil, "Comparing Clusterings-An Axiomatic View," Proc. 22nd Int'l Conf. Machine Learning, pp. 577-584, 2005.
46. P. Hansen and N. Mladenovic, "Variable Neighborhood Search for the p-Median," Location Science, vol. 5, no. 4, pp. 207-226, Dec. 1997. (Pubitemid 28497349)
47. J.J. Hull, "A Database for Handwritten Text Recognition Research," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 5, pp. 550-554, May 1994.
48. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-Based Learning Applied to Document Recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.