Abstract
As an alternative to the conventional Hebb-type unsupervised learning, differential learning was studied in the domain of Hebb’s rule [1] and decorrelation [2]. In this paper we present an ICA algorithm which employs differential learning, thus named as differential ICA. We derive a differential ICA algorithm in the framework of maximum likelihood estimation and random walk model. Algorithm derivation using the natural gradient and local stability analysis are provided. Usefulness of the algorithm is emphasized in the case of blind separation of temporally correlated sources and is demonstrated through a simple numerical example.
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References
Kosko, B.: Differential Hebbian learning. In: Proc. American Institute of Physics: Neural Networks for Computing. (1986) 277–282
Choi, S.: Adaptive differential decorrelation: A natural gradient algorithm. In: Proc. ICANN, Madrid, Spain (2002) 1168–1173
Amari, S., Chen, T.P., Cichocki, A.: Stability analysis of learning algorithms for blind source separation. Neural Networks 10 (1997) 1345–1351
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, Inc. (2001)
Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. John Wiley & Sons, Inc. (2002)
Choi, S.: Differential Hebbian-type learning algorithms for decorrelation and independent component analysis. Electronics Letters 34 (1998) 900–901
Attias, H., Schreiner, C.E.: Blind source separation and deconvolution: The dynamic component analysis algorithms. Neural Computation 10 (1998) 1373–1424
Amari, S.: Estimating functions of independent component analysis for temporally correlated signals. Neural Computation 12 (2000) 2083–2107
Amari, S.: Natural gradient works efficiently in learning. Neural Computation 10 (1998) 251–276
Choi, S., Cichocki, A., Amari, S.: Flexible independent component analysis. Journal of VLSI Signal Processing 26 (2000) 25–38
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Choi, S. (2003). Differential ICA. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_9
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DOI: https://doi.org/10.1007/3-540-44989-2_9
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