A Fast Fixed-Point Algorithm for Independent Component Analysis

Neural Computation

Volumn 9, Issue 7, 1997, Pages 1483-1492

  Hyvärinen, Aapo ^a   Oja, Erkki ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0346307721     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/neco.1997.9.7.1483     Document Type: Article

References (16)

1. Amari, S., Cichocki, A., & Yang, H. (1996). A new learning algorithm for blind source separation. In Advances in Neural Information Processing 8 (Proc. NIPS'95). Cambridge, MA: MIT Press.
2. Bell, A., & Sejnowski, T. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7, 1129-1159.
3. Bell, A., & Sejnowski, T. (1996). Learning higher-order structure of a natural sound. Network, 7, 261-266.
4. Bell, A., & Sejnowski, T. (in press). The "independent components" of natural scenes are edge filters. Vision Research.
5. Cardoso, J.-F. (1990). Eigen-structure of the fourth-order cumulant tensor with application to the blind source separation problem. In Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (pp. 2655-2658). Albuquerque, NM.
6. Cardoso, J.-F. (1992). Iterative techniques for blind source separation using only fourth-order cumulants. In Proc. EUSIPCO (pp. 739-742). Brussels.
7. Comon, P. (1994). Independent component analysis - a new concept? Signal Processing, 36, 287-314.
8. Delfosse, N., & Loubaton, P. (1995). Adaptive blind separation of independent sources: A deflation approach. Signal Processing, 45, 59-83.
9. Hurri, J., Hyvärinen, A., Karhunen, J., & Oja, E. (1996). Image feature extraction using independent component analysis. In Proc. NORSIG'96. Espoo, Finland.
10. Hyvärinen, A. (1997a). A family of fixed-point algorithms for independent component analysis. In Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP '97) (pp. 3917-3920). Munich, Germany.
11. Hyvärinen, A. (1997b). Independent component analysis by minimization of mutual information (Technical Report). Helsinki: Helsinki University of Technology, Laboratory of Computer and Information Science.
12. Hyvärinen, A., & Oja, E. (1996). A neuron that learns to separate one independent component from linear mixtures. In Proc. IEEE Int. Conf. on Neural Networks (pp. 62-67). Washington, D.C.
13. Jutten, C., & Herault, J. (1991). Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing, 24, 1-10.
14. Moreau, E., & Macchi, O. (1993). New self-adaptive algorithms for source separation based on contrast functions. In Proc. IEEE Signal Processing Workshop on Higher Order Statistics (pp. 215-219). Lake Tahoe, NV.
15. Oja, E., & Karhunen, J. (1995). Signal separation by nonlinear Hebbian learning. In M. Palaniswami, Y. Attikiouzel, R. Marks, D. Fogel, & T. Fukuda (Eds.), Computational intelligence - a dynamic system perspective (pp. 83-97). New York: IEEE Press.
16. Shalvi, O., & Weinstein, E. (1993). Super-exponential methods for blind deconvolution. IEEE Trans. on Information Theory, 39(2), 504-519.