Abstract
In this paper we propose a new preprocessing method for smooth component analysis (SmCA). The smoothness measure used in SmCA depends on the signal extreme values directly. We propose the min/max transformation based on the extreme value distribution providing the more realistic and useful signal characteristic in terms of the smoothness. The full methodology is applied as an ensemble method for the energy load prediction improvement.
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References
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing. John Wiley, Chichester (2002)
Cichocki, A., Żurada, J.M.: Blind Signal Separation and Extraction: Recent Trends, Future Perspectives, and Applications. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 30–37. Springer, Heidelberg (2004)
Evans, M., Hastings, N., Peacock, B.: Statistical Distributions, 3rd edn. John Wiley and Sons, Chichester (2000)
Haykin, S.: Adaptive filter theory, 3rd edn. Prentice-Hall, Upper Saddle River (1996)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley, Chichester (2001)
Johnson, N.L., Kotz, S., Kemp, A.W.: Univariate Discrete Distributions, 2nd edn. John Wiley and Sons, Chichester (1992)
Lendasse, A., Cottrell, M., Wertz, V., Verdleysen, M.: Prediction of Electric Load using Kohonen Maps – Application to the Polish Electricity Consumption. In: Proc. Am. Control Conf., Anchorage AK, pp. 3684–3689 (2002)
Nikias, C.L., Shao, M.: Signal Processing with Alpha-Stable Distributions and Applications. John Wiley &Son, Chichester (1995)
Samorodnitskij, G., Taqqu, M.: Stable non-Gaussian random processes: stochastic models with infinitive variance. Chapman and Hall, NY (1994)
Scales, L.E.: Introduction to Non-Linear Optimization. Springer, New York (1985)
Szupiluk, R., Wojewnik, P., Zabkowski, T.: Model Improvement by the Statistical Decomposition. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 1199–1204. Springer, Heidelberg (2004)
Szupiluk, R., Wojewnik, P., Zabkowski, T.: Smooth Component Analysis as Ensemble Method for Prediction Improvement. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds.) ICA 2007. LNCS, vol. 4666, pp. 277–284. Springer, Heidelberg (2007)
Therrien, C.W.: Discrete Random Signals and Statistical Signal Processing. Prentice Hall, New Jersey (1992)
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Szupiluk, R., Wojewnik, P., Ząbkowski, T. (2008). Generalized Extreme Value for Smooth Component Analysis in Prediction Improvement. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85563-7_94
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DOI: https://doi.org/10.1007/978-3-540-85563-7_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85562-0
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