A modified partial robust M-regression to improve prediction performance for data with outliers

IEEE International Symposium on Industrial Electronics

Volumn , Issue , 2013, Pages

  Yin, Shen ^a,b   Wang, Guang ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b BOHAI UNIVERSITY   (China)

Author keywords

prediction accuracy; PRM; tSL center; tSL distance

Indexed keywords

ACCURATE PREDICTION; COMPUTATIONAL LOADS; PREDICTION ACCURACY; PREDICTION PERFORMANCE; PRM; ROBUST LINEAR REGRESSION; TSL-CENTER; TSL-DISTANCE;

FORECASTING; INDUSTRIAL ELECTRONICS; ITERATIVE METHODS; LINEAR REGRESSION; MATHEMATICAL MODELS;

STATISTICS;

EID: 84881649533     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISIE.2013.6563843     Document Type: Conference Paper

References (17)

1. P. Geladi and B. Kowalski, "Partial least-squares regression: a tutorial," Analytica chimica acta, vol. 185, pp. 1-17, 1986.
2. S. de Jong, "Simpls: an alternative approach to partial least squares regression," Chemometrics and Intelligent Laboratory Systems, vol. 18, no. 3, pp. 251-263, 1993.
3. S. Yin, S. X. Ding, P. Zhang, A. Haghani Abandan Sari, and A. Naik, "Study on modifications of pls approach for process monitoring," in the 18th IFAC World Congress, vol. 18, no. 1, pp. 12389-12394, 2011.
4. D. Zhou, G. Li, and S. Qin, "Total projection to latent structures for process monitoring," AIChE Journal, vol. 56, no. 1, pp. 168-178, 2010.
5. I. Wakelinc and H. Macfie, "A robust pls procedure," Journal of Chemometrics, vol. 6, no. 4, pp. 189-198, 1992.
6. D. Cummins and C. Andrews, "Iteratively reweighted partial least squares: a performance analysis by monte carlo simulation," Journal of Chemometrics, vol. 9, no. 6, pp. 489-507, 1995.
7. M. Hubert and K. Branden, "Robust methods for partial least squares regression," Journal of Chemometrics, vol. 17, no. 10, pp. 537-549, 2003.
8. S. Serneels, C. Croux, P. Filzmoser, and P. Van Espen, "Partial robust m-regression," Chemometrics and Intelligent Laboratory Systems, vol. 79, no. 1, pp. 55-64, 2005.
9. M. Daszykowski, S. Serneels, K. Kaczmarek, P. Van Espen, C. Croux, and B. Walczak, "Tomcat: A matlab toolbox for multivariate calibration techniques," Chemometrics and intelligent laboratory systems, vol. 85, no. 2, pp. 269-277, 2007.
10. M. Liu, B. Vemuri, S. Amari, and F. Nielsen, "Total bregman divergence and its applications to shape retrieval," in 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3463-3468, 2010.
11. M. Daszykowski, Y. Vander Heyden, and B. Walczak, "Robust partial least squares model for prediction of green tea antioxidant capacity from chromatograms," Journal of Chromatography A, vol. 1176, no. 1, pp. 12-18, 2007.
12. S. Moller, J. von Frese, and R. Bro, "Robust methods for multivariate data analysis," Journal of Chemometrics, vol. 19, no. 10, pp. 549-563, 2006.
13. S.-i. Amari, "Differential geometrical theory of statistics," IMS Monograph vol. 10, Differential Geometry in Statistical Inference, pp. 20-94, 1987.
14. A. Banerjee, C. Krumpelman, J. Ghosh, S. Basu, and R. Mooney, "Model-based overlapping clustering," in Proceedings of the 11th ACM SIGKDD international conference on Knowledge discovery in data mining, pp. 532-537, 2005.
15. F. Nielsen and R. Nock, "On the smallest enclosing information disk," Information Processing Letters, vol. 105, no. 3, pp. 93-97, 2008.
16. B. Vemuri, M. Liu, S. Amari, and F. Nielsen, "Total bregman divergence and its applications to dti analysis," IEEE Transactions on Medical Imaging, vol. 30, no. 2, pp. 475-483, 2011.
17. M. Liu, B. Vemuri, S. Amari, and F. Nielsen, "Shape retrieval using hierarchical total bregman soft clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, no. 99, pp. 1-1, 2012.