Abstract
In this paper we show that applying of multidimensional decompositions can improve the modelling results. The predictions usually consist of twofold elements, wanted and destructive ones. Rejecting of the destructive components should improve the model. The statistical methods like PCA and ICA with new modifications are employed. The example from the telecom market proofs correctness of the approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Amari S., Cichocki A., and Yang H.H. (1996) A new learning algorithm for blind signal separation. Advances in Neural Information Processing Systems, NIPS-1995 8, MIT Press, Cambridge, MA, 757–763
Bradley A.P. (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145–1159
Cichocki A., Amari S. (2002) Adaptive Blind Signal and Image Processing. John Wiley, Chichester
Cichocki A., Sabala I., Choi S., Orsier B., Szupiluk R. (1997) Self adaptive independent component analysis for sub-Gaussian and super-Gaussian mixtures with unknown number of sources and additive noise. NOLTA-97 2, Hawaii, USA, 731–734
Cruces S., Cichocki A., Castedo L. (1999) An iterative inversion method for blind source separation. Proc. of the 1st Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA’99), Assois, France, 307–312
Greene W.H. (2000) Econometric analysis, NJ Prentice Hall
Groth R., Data Mining. Building Competitive Advantage, Prentice Hall Inc., Upper Saddle River, New Jersey
Hoeting J., Madigan D., Raftery A., and Volinsky C. (1999) Bayesian model averaging: a tutorial. Statistical Science 14, 382–417
Hyvärinen A., Karhunen J., Oja E. (2001) Independent Component Analysis. John Wiley
Jolliffe I.T. (2002) Principal Component Analysis. Springer Verlag
Kennedy R.L. (ed.), Lee Y., van Roy B., Reed C., and Lippman R.P. (1997) Solving Data Mining Problems with Pattern Recognition. Prentice Hall
Kidmose P. (2000) Alpha-Stable Distributions in Signal Processing of Audio Signals. 41st Conf. on Simulation and Modeling, Scandinavian Simulation Society SIMS, 87–94
Kidmose P. (2001) Independent Component Analysis Using the Spectral Measure for Alpha-Stable Distributions. Proc. of IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP
Nikias C.L., Shao M. (1995) Signal Processing with Alpha-Stable Distributions and Applications. John Wiley and Sons
Samorodnitskij G., Taqqu M.S. (1994) Stable non-Gaussian random processes: stochastic models with infinitive variance. Chapman and Hall, New York
Schwarz G. (1978) Estimating the Dimension of a Model. The Annals of Statistics 6, 461–471
Szupiluk R., Wojewnik P., Zźbkowski T. (2004) Independent Component Analysis for Filtration in Data Mining. Proc. of IIPWM’04 Conf., Zakopane, Poland. Advances in Soft Computing, Springer Verlag, Berlin, 117–128
Szupiluk R., Wojewnik P., Zźbkowski T. (2004) Model Improvement by the Statistical Decomposition. Artificial Intelligence and Soft Computing-ICAISC 2004. Lecture Notes in Computer Science, Springer-Verlag, Heidelberg, 1199–1204
Szupiluk R., Wojewnik P., Zźbkowski T., Siwek K. (2004) Independent Component Analysis with α-Stable Distributions. VI International Workshop Computational Problems of Electrical Engineering, Zakopane, Poland
Therrien C.W. (1992) Discrete Random Signals and Statistical Signal Processing. Prentice Hall, New Jersey
Yang Y. (2001) Adaptive regression by mixing. Journal of American Statistical Association 96, 574–588
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Szupiluk, R., Wojewnik, P., Zőbkowski, T. (2005). PCA and ICA Methods for Prediction Results Enhancement. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32392-9_40
Download citation
DOI: https://doi.org/10.1007/3-540-32392-9_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25056-2
Online ISBN: 978-3-540-32392-1
eBook Packages: EngineeringEngineering (R0)