Approximate kernel competitive learning

Neural Networks

Volumn 63, Issue , 2015, Pages 117-132

  Wu, Jian Sheng ^a,b   Zheng, Wei Shi ^a,c   Lai, Jian Huang ^a,d  

^a SUN YAT SEN UNIVERSITY   (China)

^b SYSU CMU Shunde International Joint Research Institute   (China)

^c Guangdong Province Key Laboratory of Computational Science   (China)

^d Guangdong Province Key Laboratory of Information Security   (China)

Author keywords

Kernel clustering; Kernel competitive learning; Large scale computation

Indexed keywords

DATA HANDLING; PARALLEL PROGRAMMING;

CLUSTERING APPROACH; COMPETITIVE LEARNING; COMPUTATIONAL COSTS; EMPIRICAL EVALUATIONS; KERNEL CLUSTERING; LARGE SCALE CLUSTERING; LARGE SCALE COMPUTATION; LARGE-SCALE DATA PROCESSING;

COMPUTATIONAL COMPLEXITY;

ACCURACY; APPROXIMATE KERNEL COMPETITIVE LEARNING; ARTICLE; INTERMETHOD COMPARISON; KERNEL METHOD; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PROCESS DEVELOPMENT; PSEUDO PARALLELLED APPROXIMATE KERNEL COMPETITIVE LEARNING; RANDOM SAMPLE; ALGORITHM; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTER PROGRAM;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; SOFTWARE;

EID: 84919951779     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2014.11.003     Document Type: Article

References (62)

1. Ahalt S.C., Krishnamurthy A.K., Chen P., Melton D.E. Competitive learning algorithms for vector quantization. Neural Networks 1990, 3(3):227-290.
2. Bădoiu, M., & Indvk, S. H. -P. P. (2002). Approximate clustering via core-sets. In Proc. of the 34th annual ACM symposium on theory of computing (pp. 250-257).
3. Banerjee A., Ghosh J. Frequency-sensitive competitive learning for scalable balanced clustering on high-dimensional hyperspheres. IEEE Transactions on Neural Networks 2004, 15(3):702-719.
4. Boureau, Y. -L., Bach, F., Lecun, Y., & Ponce, J. (2010). Learning mid-level features for recognition. In Proc. of IEEE int. conf. on computer vision and pattern recognition (pp. 2559-2566).
5. Bradley P.S., Bennett K.P., Demiriz A. Constrained k-means clustering, Tech. Rep. MSR-TR-2000-65 2000, Microsoft Research.
6. Chen W.-Y., Song Y., Bai H., Lin C.-J., Chang E.Y. Parallel spectral clustering in distributed systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 2011, 33(3):568-586.
7. Chitta, R., Jin, R., Havens, T. C., & Jain, A. K. (2011). Approximate kernel k-means: solution to large scale kernel clustering. In Proc. of the 17th ACM SIGKDD int. conf. on knowledge discovery and data mining (pp. 895-903).
8. Chitta, R., Jin, R., & Jain, A. K. (2012). Efficient kernel clustering using random fourier features. In Proc. of the 12th IEEE int. conf. on data mining (pp. 161-170).
9. Cordeiro, R.L. F., Traina, C. Jr., Traina, A. J. M., López, J., Kang, U., & Faloutsos, C. (2011). Clustering very large multi-dimensional datasets with mapreduce. In Proc. of the 17th ACM SIGKDD int. conf. on knowledge discovery and data mining (pp. 690-698).
10. Cottrell M., Hammer B., Hasenfuß A., Villmann T. Batch and median neural gas. Neural Networks 2006, 19(6):762-771.
11. Deng, J., Dong, W., Socher, R., Li, L. -J., Li, K., & Fei-Fei, L. (2009). Imagenet: a large-scale hierarchical image database. In Proc. of IEEE int. conf. on computer vision and pattern recognition (pp. 248-255).
12. Desieno, D. (1988). Adding a conscience to competitive learning. In Proc. of IEEE int. conf. on neural networks (pp. 117-124).
13. Dhillon, I. S., Guan, Y., & Kulis, B. (2004). Kernel k-means, spectral clustering and normalized cuts. In Proc. of the 10th ACM SIGKDD int. conf. on knowledge discovery and data mining (pp. 551-556).
14. Dhillon I.S., Guan Y., Kulis B. Weighted graph cuts without eigenvectors a multilevel approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 2007, 29(11):1944-1957.
15. Dhillon, I. S., Mallela, S., & Modha, D. S. (2003). Information-theoretic co-clustering. In Proc. of the 9th ACM SIGKDD int. conf. on knowledge discovery and data mining (pp. 89-98).
16. Digital Universe Study (2010).
17. Ene, A., Im, S., & Moseley, B. (2011). Fast clustering using mapreduce. In Proc. of the 17th ACM SIGKDD int. conf. on knowledge discovery and data mining(pp. 681-689).
18. Fei-Fei L., Fergus R., Perona P. One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence 2006, 28(4):594-611.
19. Fei-Fei, L., & Perona, P. (2005). A Bayesian hierarchical model for learning natural scene categories. In Proc. of IEEE int. conf. on computer vision and pattern recognition (pp. 524-531).
20. Fort, J. -C., Letremy, P., & Cottrell, M. (2002). Advantages and drawbacks of the batch kohonen algorithm. In Proc. of the 10th European symposium on artificial neural networks (pp. 223-230).
21. Fowlkes C., Belongie S., Chung F., Malik J. Spectral grouping using the Nyström method. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26(2):214-225.
22. Fritzke, B. (1997). Some competitive learning methods.
23. Griffin G., Holub A., Perona P. Caltech-256 object category dataset, Tech. Rep. 7694 2007, California Institute of Technology.
24. Guha, S., Rastogi, R., & Shim, K. (1998). Cure: an efficient clustering algorithm for large databases. In Proc. of 1998 ACM SIGMOD int. conf. on management of data (pp. 73-84).
25. Huang Z. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery 1998, 2(3):283-304.
26. Inokuchi R., Miyamoto S. Kernel methods for clustering: competitive learning and c-means. International Journal of Uncertainly, Fuzziness and Knowledge-Based Systems 2006, 14(4):481-493.
27. Jain A.K. Data clustering: 50 years beyond k-means. Pattern Recognition Letters 2010, 31(8):651-666.
28. Kar, P., & Karnick, H. (2012). Random feature maps for dot product kernels. In Proc. of the 15th int. conf. on artificial intelligence and statistics (pp. 583-591).
29. Kashima H., Ide T., Kato T., Sugiyama M. Recent advances and trends in large-scale kernel methods. IEICE Transactions on Information and Systems 2009, E92-D(7):1338-1353.
30. Kaufman L., Rousseeuw P.J. Finding groups in data: an introduction to cluster analysis 2005, Wiley.
31. Kohonen T. The self-organizing map. Proceedings of the IEEE 1990, 78(9):1464-1480.
32. Lay D.C. Linear algebra and its applications 2002, Addison Wesley. 3rd ed.
33. Lazebnik, S., Schmid, C., & Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. of IEEE int. conf. on computer vision and pattern recognition (pp. 2169-2178).
34. Lowe, D. G. (1999). Object recognition from local scale-invariant features. In Proc. of IEEE the 7th int. conf. on computer vision (pp. 1150-1157).
35. Lühr S., Lazarescu M. Incremental clustering of dynamic data streams using connectivity based representative points. Data & Knowledge Engineering 2009, 68(1):1-27.
36. MacDonald, D., & Fyfe, C. (2000). The kernel self organising map. In Proc. of the 4th int. conf. on knowledge-based intelligent engineering systems and allied technologies (pp. 317-320).
37. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proc. of the 5th Berkely Symposium on Mathematical Statistics and Probability (pp. 281-297).
38. Martinetz T.M., Berkovich S.G., Schulten K.J. "Neural-gas" network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks 1993, 4(4):558-569.
39. Mizutani, K., & Miyamoto, S. (2005). Kernel-based fuzzy competitive learning clustering. In Proc. of the 14th IEEE int. conf. on fuzzy systems (pp. 636-639).
40. Ng R.T., Han J. Clarans: a method for clustering objects for spatial data mining. The IEEE Transactions on Knowledge and Data Engineering 2002, 14(5):1003-1016.
41. Ng, A. Y., Jordan, M. I., & Weiss, Y. (2001). On spectral clustering: Analysis and an algorithm. In Proc. of advances in neural information processing systems(pp. 849-856).
42. Nistér, D., & Stewénius, H. (2006). Scalable recognition with a vocabulary tree. In Proc. of IEEE int. conf. on computer vision and pattern recognition (pp. 2161-2168).
43. Ordonez C., Omiecinski E. Efficient disk-based k-means clustering for relational databases. The IEEE Transactions on Knowledge and Data Engineering 2004, 16(8):909-921.
44. Pham, N., & Pagh, R. (2013). Fast and scalable polynomial kernels via explicit feature maps. In Proc. of the 19th ACM SIGKDD int. conf. on knowledge discovery and data mining (pp. 239-247).
45. Philbin, J., Chum, O., Isard, M., Sivic, J., & Zisserman, A. (2007). Object retrieval with large vocabularies and fast spatial matching. In Proc. of IEEE int. conf. on computer vision and pattern recognition (pp. 1-8).
46. Qin, A. K., & Suganthan, P. N. (2004). Kernel neural gas algorithms with application to cluster analysis. In Proc. of the 17th int. conf. on pattern recognition (pp. 617-620).
47. Rahimi, A., & Recht, B. (2007). Random features for large-scale kernel machines. In Proc. of advances in neural information processing systems (pp. 1177-1184).
48. Schleif F.-M., Zhu X., Hammer B. Soft competitive learning for large data sets. Advances in intelligent systems and computing 2013, Vol. 185:141-151.
49. Schleif, F. -M., Zhu, X., Gisbrecht, A., & Hammer, B. (2012). Fast approximated relational and kernel clustering. In Proc. of the 21th int. conf. on pattern recognition (pp. 1229-1232).
50. Schölkopf B., Smola A., Müller K.-R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 1998, 10(5):1299-1319.
51. Tsang I.W., Kwok J.T., Cheung P.-M. Core vector machines: fast SVM training on very large data sets. Journal of Machine Learning Research 2005, 6:363-392.
52. van Gemert J.C., Veenman C.J., Smeulders A.W.M., Geusebroek J.-M. Visual word ambibuity. IEEE Transactions on Pattern Analysis and Machine Intelligence 2010, 32(7):1271-1283.
53. Vesanto J., Alhoniemi E. Clustering of the self-organizing map. IEEE Transactions on Neural Networks 2000, 11(3):586-600.
54. Wang, C. -D., Lai, J. -H., & Zhu, J. -Y. (2010). A conscience on-line learning approach for kernel-based clustering. In Proc. of the 10th IEEE int. conf. on data mining(pp. 531-540).
55. Wang C.-D., Lai J.-H., Zhu J.-Y. Graph-based multiprototype competitive learning and its applications. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 2012, 42(6):934-946.
56. Wang, J., Wang, J., Ke, Q., Zeng, G., & Li, S. (2012). Fast approximate k-means via cluster closures. In Proc. of IEEE int. conf. on computer vision and pattern recognition (pp. 3037-3044).
57. Williams, C. K. I., & Seeger, M. (2001). Using the nyström method to speed up kernel machines. In Proc. of advances in neural information processing systems (pp. 682-688).
58. Xu L., Krzyzak A., Oja E. Rival penalized competitive learning for clustering analysis, RBF net, and curve detection. IEEE Transactions on Neural Networks 1993, 4(4):636-649.
59. Xu R., Wunsch D. Survey of clustering algorithms. IEEE Transactions on Neural Networks 2005, 16(3):645-678.
60. Zhang, Q., Liu, J., & Wang, W. (2007). Incremental subspace clustering over multiple data streams. In Proc. of the 7th IEEE int. conf. on data mining (pp. 727-732).
61. Zhang, T., Ramakrishnan, R., & Livny, M. (1996). Birch: an efficient data clustering method for very large databases. In Proc. of 1996 ACM SIGMOD int. conf. on management of data (pp. 103-114).
62. Zhang, R., & Rudnicky, A. I. (2002). A large scale clustering scheme for kernel k-means. In Proc. of the 16th int. conf. on pattern recognition (pp. 289-292).