Sparse Ridgelet Kernel Regressor and its online sequential extreme learning

Neurocomputing

Volumn 134, Issue , 2014, Pages 173-180

  Yang, Shuyuan ^a   Yang, Lixia ^a   Feng, Zhixi ^a   Wang, Min ^a   Jiao, Licheng ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Multiscale geometric analysis; Online Sequential Extreme Learning Algorithm; Sparse Ridgelet Kernel Regressor

Indexed keywords

LEARNING ALGORITHMS;

HIGH DIMENSIONAL DATA; ITS EFFICIENCIES; KERNEL REGRESSORS; MULTI-SCALE GEOMETRIC ANALYSIS; ONLINE LEARNING ALGORITHMS; SEQUENTIAL LEARNING; TIME SERIES PREDICTION; TRAINING PROCEDURES;

E-LEARNING;

ARTICLE; CONTROLLED STUDY; EXTREME MACHINE LEARNING; INTERMETHOD COMPARISON; LEARNING ALGORITHM; MACHINE LEARNING; MACKY GLASS TIME SERIES; ONLINE SEQUENTIAL EXTREME LEARNING ALGORITHM; PREDICTION AND FORECASTING; PRIORITY JOURNAL; SIMULATION; SPARSE RIDGELET KERNEL REGRESSOR; SPARSE WAVELET KERNEL REGRESSOR; TIME SERIES ANALYSIS;

EID: 84896544553     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.12.066     Document Type: Article

References (52)

1. Grimm L.G., Yarnold P.R. Reading and Understanding More Multidimensional Statistics 2000, American Psychological Association, Washington, DC.
2. Härdle W. Applied Nonparametric Regression 1990, Cambridge University Press, Cambridge, England.
3. Hastie T., Tibsharani R., Friedman J. The Elements of Statistical Learning 2001, Springer, New York.
4. Lorentz G.G., Golitschek M.V., Makovoz Y. Constructive Approximation: Advanced Problems 1996, Springer-Verlag, New York.
5. Bishop C.M. Neural Networks for Pattern Recognition 1995, Oxford University Press, Oxford.
6. J.N. Bank, O.A. Omitaomu, S.J. Fernandez, Y. Liu, Visualization and classification of power system frequency data streams, in: Proceedings of IEEE ICDM Workshop Spatial Spatiotemporal Data Mining, Miami, FL, December 6 2009, pp. 650-655.
7. Aronszajn N. Theory of reproducing kernels. Trans. Am. Math. Soc. 1950, 68.
8. Aizerman M.A., Braverman E.M., Rozonoer L.I. The method of potential functions for the problem of restoring the characteristic of a function converter from randomly observed points. Autom. Remote Control 1964, 25(12):1546-1556.
9. Kimeldorf G., Wahba G. Some results on Tchebycheffian spline functions. J. Math. Anal. Appl. 1971, 33:82-95.
10. Wahba G. Spline Models for Observational Data 1990, SIAM, Philadelphia, PA.
11. Duttweiler D.L., Kailath T. (1973) An RKHS approach to detection and estimation theory: Some parameter estimation problems (Part V). IEEE Trans. Inf. Theory 1973, 19(1):29-37.
12. Schölkopf B., Burges J.C., Smola A.J. Advances in Kernel Methods 1999, MIT Press, Cambridge, MA.
13. Schölkopf B., Smola A.J. Learning With Kernels 2002, MIT Press, Cambridge,MA.
14. Theodoridis S., Koutroumbas K. Pattern Recognition 2006, Academic Press, Amsterdam, The Netherlands. 3rd ed.
15. Ma J., James T., Simon P. Accurate on-line support vector regression. Neural Comput. 2003, 15(11):2683-2703.
16. E.J. Candès, Ridgelets and Their Derivatives: Representation of Images with Edges, Curves and Surfaces, Report.
17. Candès E.J. Ridgelets and the Representation of Mutilated Sobolev Functions. SIAM J. Math. Anal. 2002, 33(2):347-368.
18. Starck J.L., Candès E.J., Donoho D.L. The curvelet transform for image denoising. IEEE Trans. Image Process. 2002, 11(6):670-684.
19. Starck J.L., Murtagh F., Candès E.J., Donoho D.L. Gray and color image contrast enhancement by the curvelet transform. IEEE Trans. Image Process. 2003, 12(6):706-717.
20. Shawe-Taylor J., Cristianini N. Kernel Methods for Pattern Analysis. Cambridge 2004, Cambridge University Press, U.K.
21. Y.W. Zhang; T.Y. Chai; Z.Y. Hu(2011), On-line batch process monitoring using a consecutively updated hierarchical kernel partial least squares model, in: Proceedings of the 2011 International Symposium on Advanced Control of Industrial Processes (ADCONIP), pp.603-608.
22. Sebald D.J., Bucklew J.A. Support vector machine techniques for nonlinear equalization. IEEE Trans. Signal Process. 2000, 48:3217-3226.
23. Kivinen J., Smola A.J., Williamson R.C. Online learning with kernels. IEEE Trans. Signal Process. 2004, 52(8):2165-2176.
24. Sebald D.J., Bucklew J.A. Support vector machine techniques for nonlinear equalization. IEEE Trans. Signal Process. 2000, 48(11):3217-3226.
25. G. Cauwenberghs, T. Poggio, Incremental and decremental support vector machine learning, in: Advances in Neural Information Processing Systems (NIPS) , 2000.
26. Laskov P., Gehl C., Krüger S., Müller K.-R. Incremental support vector learning: Analysis, implementation and applications. J. Mach. Learn. Res. 2006, 7:1909-1936.
27. Engel Y., Mannor S., Meir R. The kernel recursive least-squares algorithm. IEEE Trans. Signal Process. 2004, 52(8):2275-2285.
28. Malipatil A.V., Huang Y.-F., Andra S., Bennett K. Kernelized set-membership approach to nonlinear adaptive filtering. Proc. IEEE ICASSP 2005, IV:149-152.
29. K. Crammer, J. Kandola, Y. Singer, Online classification on a budget. in: Advances in Neural Information Processing Systems 2003.
30. J. Weston, A. Bordes, L. Bottou, Online (and offline) on an even tighter budget, in: Robert G.Cowell, Zoubin Ghahramani, (Eds.), Proceedings of AISTATS, 2005, pp. 413-420.
31. Kivinen J., Smola A., Williamson R. Online learning with kernels. IEEE Trans. Signal Process. 2004, 52(8):2165-2176.
32. Cheng L., Vishwanathan S.V.N., Schuurmans D., Wang S., Caelli T. Implicit online learning with kernels. Adv. Neural Inf. Process. Syst. 2007, 249-256.
33. Dekel O., Shwartz S.S., Singer Y. The Forgetron: a kernel-based perceptron on a budget. SIAM J. Comput. 2007, 37(5):1342-1372.
34. N.C. Bianchi, A. Conconi, C. Gentile Tracking the best hyperplane with a simple budget perceptron, in: Proceedings of the 19th Conference on Learning Theory, 2006, pp. 483-498.
35. Langford J., Li L., Zhang T. Sparse online learning via truncated gradient. Adv. Neural Inf. Process. Syst. 2008, 905-912.
36. Yang S.Y., Wang M., Jiao L.C. Ridgelet Kernel Regression. NeuroComputing 2007, 70:3046-3055.
37. Huang G.B., Ding X., Zhou H. Optimization method based extreme learning machine for classification. Neurocomputing 2010, 74:155-163.
38. G.B. Huang, N.Y. Liang, H.J. Rong, P. Saratchandran, N. Sundararajan, On-line sequential extreme learning machine, in: International Conference on Computational Intelligence (CI) , Calgary, Canada, July 4-6, 2005.
39. Rong H.J., Huang G.B., Saratchandran P., Sundararajan N. On-line sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans. Syst. Man Cybern.: Pt. B 2009, 39(4):1067-1072.
40. Lan Y., Soh Y.C., Huang G.B. Ensemble of online sequential extreme learning machine. Neurocomputing 2009, 72:3391-3395.
41. Zhang L., Zhou W.D., Jiao L.C. Wavelet support vector machine. IEEE Trans. Syst. Man Cybern. Pt. B: Cybern. 2004, 34(1):34-39.
42. Murthy V.S., Tarakanath K., Mohanta D.K., Gupta S. Insulator condition analysis for overhead distribution lines using combined wavelet support vector machine (SVM). IEEE Trans. Dielectr. Electr. Insul. 2010, 17(1):89-99.
43. Candès, Ridgelets: Estimating with Ridge Functions, Techinal Report, 2003.
44. Yang S.Y., Wang M., Jiao L.C. Geometrical multi-resolution network based on ridgelet frame. Signal Process. 2007, 87(4):750-761.
45. Yang S.Y., Wang M., Jiao L.C. A linear ridgelet neural network. NeuroComputing 2009, 73(1-3):468-477.
46. Yang S.Y., Wang M., Jiao L.C. Incremental constructive ridgelet neural network. NeuroComputing 2008, 72(1-3):367-377.
47. Haykin S. Neural Networks: A Comprehensive Foundation 1999, IEEE Computer Society Press, New York. second edition.
48. Kimeldorf G.S., Wahba G. Tchebycheffian spline functions. J. Math. Anal. Appl. 1971, 33:82-95.
49. Clerc M., Kennedy J. The particle swarm: explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 2002, 6:58-73.
50. IEEE Std 1159-1995, IEEE Recommended Practice for Monitoring Electric Power Quality.
51. Dugan R.C., McGranaghan M.F., Santoso S., Beaty H.W. Electrical Power System Quality 2002, McGraw- Hill, New York.
52. Kaewarsa S., et al. Recognition of power quality events by using multiwavelet-based neural networks. Electr. Power Energy Syst. 2008, 30:254-260.