Robust Algorithms for Regression Analysis Based on Fuzzy Objective Functions
In this paper, we address the issues related to the design of fuzzy robust linear regression algorithms. The design of robust linear regression analysis has been studied in the literature of statistics for over two decades. More recently various robust regression models have been proposed for processing noisy data. We proposed a new objective function by using fuzzy complement and derive improved algorithms that can produce good regression analysis from the spoiled data set. Data set from the U.S. Department of Transportation is used to evaluate the performance of the regression algorithms.
P. J. Huber and E. M. Ronchetti, Robust statistics, 2nd, New York: Wiley, 2009.
Y. Kopsinis, S. Chouvardas, and S. Theodoridis, “Iterative randomized robust linear regression,” International Conference on Acoustics, Speech and Signal Processing, pp. 5436-5540, April 2015.
G. Papageorgiou, P. Bouboulis, and S. Theodoridis, “Robust linear regression analysis—a greedy approach,” IEEE Transactions on Signal Processing, vol. 63, no. 15, pp. 3872-3887, May 2015.
D. Huang, R. Cabral, and F. De la Torre, “Robust regression,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 2, pp. 363-375, February 2016.
G. L. Cheng, F. Y. Zhu, S. M. Xiang, Y. Wang, and C. H. Pan, “Semisupervised hyperspectral image classification via discriminant analysis and robust regression,” IEEE Journal of selected topics in applied earth observations and remote sensing, vol. 9, no. 2, pp. 595-608, September 2016.
A. Nurunnabi, G. West, and D. Belton, “Robust locally weighted regression techniques for ground surface points filtering in mobile laser scanning three dimensional point cloud data,” IEEE Transaction on Geoscience and Remote Sensing, vol. 54, no. 4, pp. 2181-2193, November 2015.
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