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Mathematics in Independent Component Analysis

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300 BIBLIOGRAPHY<br />

Georgiev, P. (2001). Bl<strong>in</strong>d source separation of bil<strong>in</strong>early mixed signals. In Proc. of ICA 2001,<br />

pages 328–330, San Diego, USA.<br />

Georgiev, P., Pardalos, P., and Theis, F. (2006). A bil<strong>in</strong>ear algorithm for sparse representations.<br />

Computational Optimization and Applications.<br />

Georgiev, P., Pardalos, P., Theis, F., Cichocki, A., and Bakardjian, H. (2005a). Data M<strong>in</strong><strong>in</strong>g<br />

<strong>in</strong> Biomedic<strong>in</strong>e, chapter Sparse component analysis: a new tool for data m<strong>in</strong><strong>in</strong>g. Spr<strong>in</strong>ger, <strong>in</strong><br />

pr<strong>in</strong>t.<br />

Georgiev, P. and Theis, F. (2004). Bl<strong>in</strong>d source separation of l<strong>in</strong>ear mixtures with s<strong>in</strong>gular<br />

matrices. In Proc. ICA 2004, volume 3195 of LNCS, pages 121–128, Granada, Spa<strong>in</strong>. Spr<strong>in</strong>ger.<br />

Georgiev, P., Theis, F., and Cichocki, A. (2004). Bl<strong>in</strong>d source separation and sparse component<br />

analysis of overcomplete mixtures. In Proc. ICASSP 2004, volume 5, pages 493–496, Montreal,<br />

Canada.<br />

Georgiev, P., Theis, F., and Cichocki, A. (2005b). Multiscale Optimization Methods, chapter<br />

Optimization algorithms for sparse representations and applications. Ed. P. Pardalos.<br />

Georgiev, P., Theis, F., and Cichocki, A. (2005c). Sparse component analysis and bl<strong>in</strong>d source<br />

separation of underdeterm<strong>in</strong>ed mixtures. IEEE Transactions on Neural Networks, 16(4):992–<br />

996.<br />

Ghurye, S. and Olk<strong>in</strong>, I. (1962). A characterization of the multivariate normal distribution.<br />

Ann. Math. Statist., 33:533–541.<br />

Gruber, P., Kohler, C., and Theis, F. (2007). A toolbox for model-free analysis of fmri data. In<br />

Proc. ICA 2007, London, U.K.<br />

Gruber, P., Stadlthanner, K., Böhm, M., Theis, F., Lang, E., Tomé, A., Teixeira, A., Puntonet,<br />

C., and Saéz, J. G. (2006). Denois<strong>in</strong>g us<strong>in</strong>g local projective subspace methods. Neurocomput<strong>in</strong>g,<br />

69:1485–1501.<br />

Gruber, P. and Theis, F. (2006). Grassmann cluster<strong>in</strong>g. In Proc. EUSIPCO 2006, Florence,<br />

Italy.<br />

Gutch, H. and Theis, F. (2007). <strong>Independent</strong> subspace analysis is unique, given irreducibility.<br />

In Proc. ICA 2007, London, U.K.<br />

Hashimoto, W. (2003). Quadratic forms <strong>in</strong> natural images. Network: Computation <strong>in</strong> Neural<br />

Systems, 14:765–788.<br />

Hayk<strong>in</strong>, S. (1994). Neural networks. Macmillan College Publish<strong>in</strong>g Company.<br />

Hérault, J. and Jutten, C. (1986). Space or time adaptive signal process<strong>in</strong>g by neural network<br />

models. In Denker, J., editor, Neural Networks for Comput<strong>in</strong>g. Proceed<strong>in</strong>gs of the AIP<br />

Conference, pages 206–211, New York. American Institute of Physics.

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