K-plane regression

Information Sciences

Volumn 292, Issue , 2015, Pages 39-56

  Manwani, Naresh ^a   Sastry, P S ^b  

^a GE GLOBAL RESEARCH   (India)

^b INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

Cluster wise linear regression; Expectation maximization; Mixture of experts; Piecewise linear regression

Indexed keywords

ALGORITHMS; LINEAR REGRESSION; MAXIMUM LIKELIHOOD; MAXIMUM LIKELIHOOD ESTIMATION; MAXIMUM PRINCIPLE; PIECEWISE LINEAR TECHNIQUES; REGRESSION ANALYSIS;

EXPECTATION - MAXIMIZATIONS; K-MEANS CLUSTERING ALGORITHM; LINEAR MODELING; MIXTURE OF EXPERTS; PIECE-WISE LINEAR FUNCTIONS; PIECEWISE LINEAR REGRESSION; PLANE REGRESSION; PROBABILITY MODELING;

CLUSTERING ALGORITHMS;

EID: 84922605939     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2014.08.058     Document Type: Article

References (33)

1. E. Amaldi, and M. Mattavelli The MIN PFS problem and piecewise linear model estimation Discrete Appl. Math. 118 1-2 2002 115 143
2. S. An, H. Shi, Q. Hu, X. Li, J. Dang, Fuzzy rough regression with application to wind speed prediction, Inform. Sci. (0), 16 April 2014.
3. A. Asuncion, D. Newman, UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences, 2007. < http://www.ics.uci.edu/∼mlearn/MLRepository.html >.
4. A.M. Bagirov, C. Clausen, and M. Kohler Estimation of a regression function by maxima of minima of linear functions IEEE Trans. Inf. Theory 55 2 2009 833 845
5. A.M. Bagirov, J. Ugon, and H. Mirzayeva Nonsmooth nonconvex optimization approach to clusterwise linear regression problems Eur. J. Oper. Res. 229 1 2013 132 142
6. M.S. Bazaraa, H.D. Sherali, and C.M. Shetty Nonlinear Programming Theory and Algorithms second ed. 2004 John Wiley & Sons Inc.
7. A. Bemporad, A. Garulli, S. Paoletti, A. Vicino, A greedy approach to identification of piecewise affine models, in: Proceedings of the 6th International Conference on Hybrid Systems: Computation and Control (HSCC), Prague, Czech Republic, 2003, pp. 97-112.
8. A. Bemporad, A. Garulli, S. Paoletti, and A. Vicino A bounded error approach to piecewise affine system identification IEEE Trans. Autom. Control 50 10 2005 1567 1580
9. C.M. Bishop Pattern Recognition and Machine Learning (Information Science and Statistics) first ed. 2006 Springer
10. L. Breiman Hinging hyperplane for regression, classification and function approximation IEEE Trans. Inform. Theory 39 3 1993 999 1013
11. L. Breiman, J. Friedman, R. Olshen, and C. Stone Classification and Regression Trees Statistics/Probability Series 1984 Wadsworth Publishing Company Belmont, California, USA
12. R.A. Carbonneau, G. Caporossi, and P. Hansen Globally optimal clusterwise regression by mixed logical-quadratic programming Eur. J. Oper. Res. 212 1 2011 213 222
13. C.-C. Chang, C.-J. Lin, LIBSVM: A Library for Support Vector Machines, Software, 2001. < http://www.csie.ntu.edu.tw/∼cjlin/libsvm/ >.
14. W.S. DeSarbo, and W.L. Cron A maximum likelihood methodology for clusterwise linear regression J. Classif. 5 2 1988 249 282
15. P. D'Urso, R. Massari, and A. Santoro A class of fuzzy clusterwise regression models Inf. Sci. 180 24 2010 4737 4762
16. T. Hastie, R. Tibshirani, and J. Friedman Elements of Statistical Learning Theory 2001 Springer
17. S. Haykin Neural Networks: A Comprehensive Foundation second ed. 2006 Pearson Education
18. K.-C. Hung, and K.-P. Lin Long-term business cycle forecasting through a potential intuitionistic fuzzy least-squares support vector regression approach Inform. Sci. 224 0 2013 37 48
19. R. Jacobs, M. Jordan, S. Nowlan, and G. Hinton Adaptive mixtures of local experts Neural Comput. 3 1 1991 79 87
20. Jayadeva, A.K. Deb, and S. Chandra Algorithm for building a neural network for function approximation IEE Proc.-Circ. Devices Syst. 149 516 2002 301 307
21. J.-T. Jeng Hybrid approach of selecting hyperparameters of support vector machine for regression IEEE Trans. Syst. Man Cybern.-Part B: Cybern. 36 3 2005 699 709
22. M.I. Jordan, and R.A. Jacobs Hierarchical mixture of experts and EM algorithm Neural Comput. 6 2 1994 181 214
23. K.-P. Lin, P.-F. Pai, Y.-M. Lu, and P.-T. Chang Revenue forecasting using a least-squares support vector regression model in a fuzzy environment Inform. Sci. 220 0 2013 196 209 online Fuzzy Machine Learning and Data Mining
24. A.B. Musa Comparative study on classification performance between support vector machine and logistic regression Int. J. Mach. Learn. Cybern. 4 2013 13 24
25. S. Paoletti, A.L. Juloski, Ferrari-Trecate, and R. Vidal Identification of hybrid systems: a tutorial Euro. J. Control 13 2/3 2007 242 260
26. P. Pucar, and J. Sjöberg On the hinge-finding algorithm for hinging hyperplanes IEEE Trans. Inform. Theory 44 3 1998 1310 1319
27. A.J. Smola, B. Schölkopf, A Tutorial on Support Vector Regression, NeuroCOLT2 Technical Report Series NC2-TR-1998-030, GMD, October 1998.
28. H. Spath A fast algorithm for clusterwise linear regression Computing 29 2 1982 175 181
29. S.R. Waterhouse, Classification and Regression using Mixtures of Experts, Ph.D. thesis, Department of Engineering, University of Cambridge, 1997.
30. Q. Wu, R. Law, E. Wu, and J. Lin A hybrid-forecasting model reducing Gaussian noise based on the Gaussian support vector regression machine and chaotic particle swarm optimization Inform. Sci. 238 0 2013 96 110
31. Y. Zhang, F. Cao, and C. Yan Learning rates of least-square regularized regression with strongly mixing observations Int. J. Mach. Learn. Cybern. 3 2012 277 283
32. Y.-P. Zhao, J.-G. Sun, Z.-H. Du, Z.-A. Zhang, and Y.-B. Li Online independent reduced least squares support vector regression Inform. Sci. 201 0 2012 37 52
33. J. Zhu, S.C.H. Hoi, and M.R.-T. Lyu Robust regularized kernel regression IEEE Trans. Syst. Man Cybern.-Part B: Cybern. 38 6 2008 1639 1644