Abstract
Independent component analysis is an important statistical tool in machine learning, pattern recognition, and signal processing. Most of these applications require on-line learning algorithms. Current on-line ICA algorithms use the stochastic gradient concept, drawbacks of which include difficulties in selecting the step size and generating suboptimal estimates. In this paper a recursive generalized eigendecomposition algorithm is proposed that tracks the optimal solution that one would obtain using all the data observed.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
- Independent Component Analysis
- Independent Component Analysis
- Blind Source Separation
- Recursive Little Square
- Independent Component Analysis Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, New York (2001)
Cichocki, A., Amari, S.I.: Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. Wiley, Chichester (2002)
Hyvärinen, A., Oja, E., Hoyer, P., Hurri, J.: Image Feature Extraction by Sparse Coding and Independent Component Analysis. In: Proceedings of ICPR 1998, pp. 1268–1273 (1998)
Lan, T., Erdogmus, D., Adami, A., Pavel, M.: Feature Selection by Independent Component Analysis and Mutual Information Maximization in EEG Signal Classification. In: Proceedings IJCNN 2005, Montreal, pp. 3011–3016 (2005)
Everson, R., Roberts, S.: Independent Component Analysis: A Flexible Nonlinearity and Decorrelating Manifold Approach. Neural Computation 11, 1957–1983 (2003)
Bell, A., Sejnowski, T.: An Information-Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation 7, 1129–1159 (1995)
Haykin, S.: Adaptive Filter Theory. Prentice Hall, Upper Saddle River, New Jersey (1996)
Cardoso, J.: Bind signal separation: Statistical principles. Proc. of the IEEE 86 (1998)
Parra, L., Sajda, P.: Blind Source Separation via Generalized Eigenvalue Decomposition. Journal of Machine Learning Research 4, 1261–1269 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ozertem, U., Erdogmus, D., Lan, T. (2006). Recursive Generalized Eigendecomposition for Independent Component Analysis. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_25
Download citation
DOI: https://doi.org/10.1007/11679363_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
Online ISBN: 978-3-540-32631-1
eBook Packages: Computer ScienceComputer Science (R0)