Sparse coding neural gas for the separation of noisy overcomplete sources

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

Volumn 5163 LNCS, Issue PART 1, 2008, Pages 788-797

  Labusch, Kai ^a   Barth, Erhardt ^a   Martinetz, Thomas ^a  

^a UNIVERSITY OF LÜBECK   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

LINEAR MIXTURES; MIXING MATRIX; NEURAL GAS; NOISE LEVELS; ORTHOGONAL MATCHING PURSUIT; OVER-COMPLETE; SPARSE CODING;

BACKPROPAGATION; MIXING; MIXTURES; NEURAL NETWORKS;

MATRIX ALGEBRA;

EID: 58849157420     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87536-9_81     Document Type: Conference Paper

References (15)

1. Labusch, K., Barth, E., Martinetz, T.: Learning Data Representations with Sparse Coding Neural Gas. In: Verleysen, M. (ed.) Proceedings of the 16th European Symposium on Artificial Neural Networks, pp. 233-238. D-Side Publishers (2008)
2. Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7(6), 1129-1159 (1995)
3. Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 10(3), 626-634 (1999)
4. Hyvarinen, A., Cristescu, R., Oja, E.: A fast algorithm for estimating overcomplete ica bases for image windows. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 1999, vol. 2, pp. 894-899 (1999)
5. Lee, T.W., Lewicki, M., Girolami, M., Sejnowski, T.: Blind source separation of more sources than mixtures using overcomplete representations. IEEE Signal Processing Letters 6(4), 87-90 (1999)
6. Lewicki, M.S., Sejnowski, T.J.: Learning Overcomplete Representations. Neural Computation 12(2), 337-365 (2000)
7. Theis, F., Lang, E., Puntonet, C.: A geometric algorithm for overcomplete linear ICA. Neurocomputing 56, 381-398 (2004)
8. Hyvärinen, A.: Gaussian moments for noisy independent component analysis. IEEE Signal Processing Letters 6(6), 145-147 (1999)
9. Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley-Interscience, Chichester (2001)
10. Pati, Y., Rezaiifar, R., Krishnaprasad, P.: Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In: Proceedings of the 27 th Annual Asilomar Conference on Signals, Systems (November 1993)
11. Donoho, D.L., Elad, M., Temlyakov, V.N.: Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Transactions on Information Theory 52(1), 6-18 (2006)
12. Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing 20(1), 33-61 (1998)
13. Rebollo-Neira, L., Lowe, D.: Optimized orthogonal matching pursuit approach. IEEE Signal Processing Letters 9(4), 137-140 (2002)
14. Oja, E.: A simplified neuron model as a principal component analyzer. J. Math. Biol. 15, 267-273 (1982)
15. Martinetz, T., Berkovich, S., Schulten, K.: "Neural-gas" Network for Vector Quantization and its Application to Time-Series Prediction. IEEE-Transactions on Neural Networks 4(4), 558-569 (1993)