Colored subspace analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4666 LNCS, Issue , 2007, Pages 121-128

  Theis, Fabian J ^a   Kawanabe, M ^b  

^a Bernstein Center for Computational Neuroscience   (Germany)

^b Fraunhofer FIRSTIDA   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BLIND SOURCE SEPARATION; DATA RECORDING; DATA STRUCTURES; DATABASE SYSTEMS; PRINCIPAL COMPONENT ANALYSIS;

COLORED SUBSPACE ANALYSIS; DATA SETS;

INDEPENDENT COMPONENT ANALYSIS;

EID: 38149020097     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74494-8_16     Document Type: Conference Paper

References (15)

1. Oja, E.: Nonlinear pea criterion and maximum likelihood in independent component analysis. In: Proc. ICA 1999, Aussois, France, pp. 143-148 (1999)
2. Mika, S., Schölkopf, B., Smola, A.J., Müller, K., Scholz, M., Rätsch, G.: Kernel PCA and de-noising in feature spaces. In: Kearns, M.S., Solla, S.A., Cohn, D.A (eds.) Advances in Neural Information Processing Systems, vol. 11, MIT Press, Cambridge (1999)
3. Tenenbaum, J., de Silva, V., Langford, J.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319-2323 (2000)
4. Blanchard, G., Kawanabe, M., Sugiyama, M., Spokoiny, V., Müller, K.: In search of non-gaussian components of a high-dimensional distribution. Journal of Machine Learning Research 7, 247-282 (2006)
5. Theis, F., Kawanabe, M.: Uniqueness of non-gaussian subspace analysis. In: Rosea, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 917-925. Springer, Heidelberg (2006)
6. Kawanabe, M., Theis, F.: Estimating non-gaussian subspaces by characteristic functions. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 157-164. Springer, Heidelberg (2006)
7. Abed-Meraim, K., Belouchrani, A.: Algorithms for joint block diagonalization. In: Proc. EUSIPCO 2004, Vienna, Austria, pp. 209-212 (2004)
8. Févotte, C., Theis, F.: Orthonormal approximate joint block-diagonalization. Technical report, GET/Télécom Paris (2007)
9. Tong, L., Liu, R.W., Soon, V., Huang, Y.F.: Indeterminacy and identifiability of blind identification. IEEETransactions on Circuits and Systems 38, 499-509 (1991)
10. Wiskott, L., Sejnowski, T.: Slow feature analysis: Unsupervised learning of invariances. Neural Computation 14, 715-770 (2002)
11. Belouchrani, A., Meraim, K.A., Cardoso, J.F., Moulines, E.: A blind source separation technique based on second order statistics. IEEE Transactions on Signal Processing 45(2), 434-444 (1997)
12. Ziehe, A., Mueller, K.R.: TDSEP - an efficient algorithm for blind separation using time structure. In: Niklasson, L., Bodén, M., Ziemke, T. (eds.) Proc. of ICANN'98, Skövde, Sweden, pp. 675-680. Springer, Berlin (1998)
13. Müller, K.R., Philips, P., Ziehe, A.: Jadetd: Combining higher-order statistics and temporal information for blind source separation (with noise). In: Proc. of ICA 1999, Aussois, France, pp. 87-92 (1999)
14. Theis, F., Inouye, Y.: On the use of joint diagonalization in blind signal processing. In: Proc. ISCAS 2006, Kos, Greece (2006)
15. Wismüller, A., Lange, O., Dersch, D., Leinsinger, G., Hahn, K., Pütz, B., Auer, D.: Cluster analysis of biomedical image time-series. International Journal on Computer Vision 46, 102-128 (2002)