Non-Parametric Kernel Learning with robust pairwise constraints

International Journal of Machine Learning and Cybernetics

Volumn 3, Issue 2, 2012, Pages 83-96

  Chen, Changyou ^a   Zhang, Junping ^b   He, Xuefang ^c   Zhou, Zhi Hua ^d  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b FUDAN UNIVERSITY   (China)

^c BEIHANG UNIVERSITY   (China)

^d NANJING UNIVERSITY   (China)

Author keywords

Graph embedding; Kernel learning; Pairwise constraint; Semi definitive programming; Semi supervised learning

Indexed keywords

CONSTRAINED GRADIENT DESCENT; CONSTRAINT GRADIENTS; DATA SETS; GRAPH EMBEDDINGS; K-MEANS; KERNEL LEARNING; KERNEL MATRICES; LAPLACIANS; LARGE-SCALE DATASETS; LEARNING SPEED; NON-PARAMETRIC; PAIRWISE CONSTRAINTS; RUNNING TIME; SEGMENTATION ACCURACY; SEMI-SUPERVISED CLUSTERING; SEMI-SUPERVISED LEARNING;

CLUSTERING ALGORITHMS; EXPERIMENTS; FUNCTIONAL PROGRAMMING; IMAGE SEGMENTATION; PROBLEM SOLVING; SUPERVISED LEARNING;

LEARNING ALGORITHMS;

EID: 84861491718     PISSN: 18688071     EISSN: 1868808X     Source Type: Journal    
DOI: 10.1007/s13042-011-0048-6     Document Type: Article

References (41)

1. Chapelle O, Schölkopf B, Zien A (2006) Semi-supervised learning. MIT Press, Cambridge.
2. Kulis B, Basu S, Dhillon I, Mooney R (2005) Semi-supervised graph clustering: a kernel approach. In: Proceedings of the 22nd international conference on machine learning, pp 457-464.
3. Li Z, Liu J, Tang X (2008) Pairwise constraint propagation by semidefinite programming for semi-supervised classification. In: Proceedings of the 25th international conference on machine learning, pp 576-583.
4. Yeung D-Y, Chang H, Dai G (2007) A scalable kernel-based algorithm for semi-supervised metric learning. In: Proceedings of the 20th international joint conference on artificial intelligence, pp 1138-1143.
5. Cortes C, Mohri M, Rostamizadeh A (2009) Learning non-linear combinations of kernels. In: Advances in neural information processing systems, vol 21.
6. Yan SC, Xu D, Zhang BY, Zhang H-J, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1): 40-51.
7. Yang J, Yan SC, Fu Y, Li XL, Huang TS (2008) Non-negative graph embedding. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition.
8. Schölkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge.
9. Cortes C, Mohri M, Rostamizadeh A (2010) Two-stage learning kernel algorithms. In: Proceedings of the 27th international conference on machine learning.
10. Cortes C, Mohri M, Rostamizadeh A (2010) Generalization bounds for learning kernels. In: Proceedings of the 27th international conference on machine learning.
11. Jin R, Hoi SCH, Yang T (2010) Online multiple kernel learning: algorithms and mistake bounds. In: Proceedings of the 21st international conference algorithmic learning theory, pp 390-404.
12. Baraldi A, Blonda P (1999) A survey of fuzzy clustering algorithms for pattern recognition-part II. IEEE Trans Syst Man Cybern Part B 29(6): 786-801.
13. Yang MS, Wu KL, Hsieh JN, Yu J (2008) Alpha-cut implemented fuzzy clustering algorithms and switching regressions. IEEE Trans Syst Man Cybern Part B 38(3): 904-915.
14. Trappey AJC, Trappey CV, Hsu F-C, Hsiao DW (2009) A fuzzy ontological knowledge document clustering methodology. IEEE Trans Syst Man Cybern Part B 39(3): 123-131.
15. Xiong H, Wu J, Chen J (2009) k-Means clustering versus validation measures: a data-distribution perspective. IEEE Trans Syst Man Cybern Part B 39(2): 318-331.
16. Basu S, Bilenko M, Mooney R (2004) A probabilistic framework for semi-supervised clustering. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 59-68.
17. Bilenko M, Basu S, Mooney R (2004) Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of the 21st international conference on machine learning, pp 81-89.
18. Kamvar SD, Klein D, Manning C (2003) Spectral learning. In: Proceedings of the 18th international joint conference on artificial intelligence, pp 561-566.
19. Xing EP, Ng AY, Jordan MI, Russell S (2003) Distance metric learning, with application to clustering with side-information. In: Advances in neural information processing systems, vol 15.
20. Wagstaff K, Cardie C, Rogers S, Schroedl S (2001) Constrained k-means clustering with background knowledge. In: Proceedings of the 18th international conference on machine learning, pp. 798-803.
21. Hong Y, Kwong S (2009) Learning assignment order of instances for the constrained k-means clustering algorithm. IEEE Trans Syst Man Cybern Part B 39(2): 568-574.
22. Lu Z, Leen TK (2005) Semi-supervised learning with penalized probabilistic clustering. In: Advances in neural information processing systems, vol 17, pp 849-856.
23. Bar-Hillel A, Hertz T, Shental N, Weinshall D (2005) Learning a Mahalanobis metric from equivalence constraints. J Mach Learn Res 6: 937-965.
24. Hertz T, Bar-Hillel A, Weinshall D (2004) Boosting margin based distance function for clustering. In: Proceedings of the 21st international conference on machine learning, pp 393-400.
25. Xu Z, Dai M, Meng D (2009) Fast and efficient strategies for model selection of Gaussian support vector machine. IEEE Trans Syst Man Cybern Part B 39(5): 1292-1307.
26. Dhillon I, Guan Y, Kulis B (2004) Kernel k-means, spectral clustering and normalized cuts. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp 551-556.
27. Bousquet O, Herrmann D (2003) On the complexity of learning the kernel matrix. In: Advances in neural information processing systems, vol 15, pp 399-406.
28. Hoi SCH, Jin R, Lyu MR (2007) Learning nonparametric kernel matrices from pairwise constraints. In: Proceedings of the 24th international conference on machine learning, pp 361-368.
29. Zhuang J, Tsang IW, Hoi SCH (2009) SimpleNPKL: simple non-parametric kernel learning. In: Proceedings of the 26th international conference on machine learning, pp 1273-1280.
30. Zhou DY, Huang J, Schölkopf B (2005) Learning from labeled and unlabeled data on a directed graph. In: Proceedings of the 22nd international conference on machine learning, pp 1036-1043.
31. Sturm JF (1999) Using SeDuMi 1. 02, a matlab toolbox for optimization over symmetric cones. Optim Methods Softw 11(2): 625-653.
32. Adler RL, Dedieu JP, Margulies JY, Martens M, Shub M (2002) Newton's method on Riemannian manifolds and a geometric model for the human spine. IMA J Numer Anal 22(3): 359-390.
33. Golub GH, Loan CFV (1996) Matrix computation. Johns Hopkins University Press, Baltimore.
34. Roweis S, Saul L (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500): 2323-2326.
35. Asuncion A, Newman DJ (2007) UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences. http://www. ics. uci. edu/~mlearn/MLRepository. html.
36. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8): 888-905.
37. Criminisi A MSRC Category Image Database (v2). MSRC. http://research. microsoft. com/en-us/um/people/antcrim/data_objrec/msrc_objcategimagedatabase_v2. zip.
38. Shi J MATLAB Normalized Cuts Segmentation Code. http://www. cis. upenn. edu/jshi/software/.
39. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2): 91-110.
40. Tan B, Zhang J, Wang L (2011) Semi-supervised elastic net for pedestrian counting. Pattern Recognit 44(10-11): 2297-2304.
41. Duchenne O, Audibert J-Y, Keriven R, Ponce J, Segonne F (2008) Segmentation by transduction. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition.