# Optimizing ICA Using Prior Information

### Giancarlo Valente, Giuseppe Filosa, Federico De Martino, Elia Formisano, Marco Balsi

#### 2005

#### Abstract

In this work we introduce a novel algorithm for Independent Component Analysis (ICA) that takes available prior information on the sources into account. This prior information is included in the form of a “weak” constraint and is exploited simultaneously with independence in order to separate the sources. Optimization is performed by means of Simulated Annealing. We show how it outperforms classical ICA algorithms in the case of low SNR. Moreover, additional prior information on the sources enforces the ordering of the components according to their significance.

#### References

- P. Comon: Independent component analysis-A new concept?, Signal Processing, (1994) 36(3), 287-314.
- T.P. Jung, S. Makeig, MJ McKeown, A.J. Bell, T.W. Lee, T.J. Sejnowski: Imaging brain dynamics using independent component analysis, Proceedings of the IEEE, (2001), 89(7) 1107-22.
- M.J. McKeown, S. Makeig, G.G. Brown, T.P. Jung,S.S. Kindermann, A.J. Bell, T.J. Sejnowski: Analysis of fMRI data by blind separation into spatial independent component analysis. Human Brain Mapping (1998), 6, 160-188.
- A. Hyvärinen, J. Karhunen, E. Oja: Independent Component Analysis, Wiley, 2001
- A. Cichocki, S.I. Amari: Adaptive Blind Signal and Image Processing, Wiley, 2002
- M.J. McKeown, T.J. Sejnowski: Independent component analysis of fMRI data: examining the assumptions. Human Brain Mapping (1998), 6, 368-372
- C. Papathanassiou, M. Petrou: Incorporating prior knowledge in ICA, Digital Signal Processing 2002. IEEE 14th Int. Conf. On, (2002) 2, 761.64.
- W. Lu, J. Rajapakse: Eliminating indeterminacy in ICA, Neurocomputing (2003), 50, 271- 290.
- V.D. Calhoun, T. Adali, M.C. Stevens, K.A. Kiehl, J.J. Pekar: Semi-blind ICA of fMRI: a method for utilizing hypothesis-derived time courses in spatial ICA analysis, Neuroimage, (2005), 25(2), 527-538.
- A. Hyvärinen: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis, IEEE Trans. on Neural Networks, (1999), 10(3), 626-634.
- S. Kirkpatrick, C. D. Gelatt, Jr., M.P. Vecchi: Optimization by simulated annealing, Science, (1983), n. 4598,.
- Handbook on algorithms and theory of computation, M.J. Atallah editor, CRC Press, (1998).
- F. Esposito, E. Formisano, E. Seifritz, R. Goebel, R. Morrone, G. Tedeschi, F. Di Salle: Spatial independent component analysis of functional MRI time-series: To what extent do results depend on the algorithm used?, Human Brain Mapping, (2002) 16(3), 146-157.

#### Paper Citation

#### in Harvard Style

Valente G., Filosa G., De Martino F., Formisano E. and Balsi M. (2005). **Optimizing ICA Using Prior Information** . In *Proceedings of the 1st International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2005)* ISBN 972-8865-35-X, pages 27-34. DOI: 10.5220/0001195800270034

#### in Bibtex Style

@conference{bpc05,

author={Giancarlo Valente and Giuseppe Filosa and Federico De Martino and Elia Formisano and Marco Balsi},

title={Optimizing ICA Using Prior Information},

booktitle={Proceedings of the 1st International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2005)},

year={2005},

pages={27-34},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0001195800270034},

isbn={972-8865-35-X},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2005)

TI - Optimizing ICA Using Prior Information

SN - 972-8865-35-X

AU - Valente G.

AU - Filosa G.

AU - De Martino F.

AU - Formisano E.

AU - Balsi M.

PY - 2005

SP - 27

EP - 34

DO - 10.5220/0001195800270034