A Case Study of ICA with Multi-scale PCA of Simulated Traffic Data

Authors:
Shengkun Xie;Pietro Lió;Anna T. Lawniczak
Affiliations:
Department of Mathematics and Statistics, University of Guelph, Guelph, Canada N1G 2W1;The Computer Laboratory, University of Cambridge, Cambridge, UK CB3 0FD;Department of Mathematics and Statistics, University of Guelph, Guelph, Canada N1G 2W1
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

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Abstract

Often packet traffic data is non-stationary and non-gaussian. These data complexity causes difficulties in its analysis by standard techniques and new methods must be employed. Recent theoretical and applied works have demonstrated the appropriateness of wavelets for analyzing multivariate signals containing non-stationarity and non- gaussianity. This paper presents a new pre-processing method, a multi-scale PCA that combines a wavelet filtering method with principal component analysis (PCA), for a noise free independent component analysis (ICA) model. By applying the proposed method to a set of test data coming from simulations of a packet switching network (PSN) model we see improvements of data analysis results.