Processing images by semi-linear predictability minimization

Network: Computation in Neural Systems

Volumn 10, Issue 2, 1999, Pages 133-169

  Schraudolph, Nicol N ^a   Eldracher, Martin ^a   Schmidhuber, Jürgen ^a  

^a DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCE IDSIA   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; ENVIRONMENT; FORECASTING; PHOTOGRAPHY; PHYSIOLOGY; VISION;

ENVIRONMENT; FORECASTING; NEURAL NETWORKS (COMPUTER); PHOTOGRAPHY; PHYSIOLOGY; VISION;

EID: 0041637282     PISSN: 0954898X     EISSN: None     Source Type: Journal    
DOI: 10.1088/0954-898X_10_2_303     Document Type: Article

References (28)

1. Amari S, Cichocki A and Yang H H 1996 A new learning algorithm for blind signal separation Advances in Neural Information Processing Systems 8 ed D S Touretzky et al (Cambridge, MA: MIT Press) pp 757-63
2. Atick J J, Li Z and Redlich AN 1992 Understanding retinal color coding from first principles Neural Comput. 4 559-72
3. Barlow H B, Kaushal T P and Mitchison G J 1989 Finding minimum entropy codes Neural Comput. 1 412-23
4. Barrow H G 1987 Learning receptive fields Proc. 1st IEEE Ann. Conf. on Neural Networks vol IV (Piscataway, NJ: IEEE) pp 115-21
5. Bell A J and Sejnowski T J 1995 An information-maximization approach to blind separation and blind deconvolution Neural Comput. 7 1129-59
6. Cardoso J-F and Souloumiac A 1993 Blind beamforming for non-Gaussian signals IEE Proc. F 140 362-70
7. Comon P 1994 Independent component analysis - a new concept? Signal Process. 36 287-314
8. Deco G and Parra L 1997 Non-linear feature extraction by redundancy reduction in an unsupervised stochastic neural network Neural Networks 10 683-91
9. Field D J 1994 What is the goal of sensory coding? Neural Comput. 6 559-601
10. Földiák P 1990 Forming sparse representations by local anti-Hebbian learning Biol. Cybern. 64 165-70
11. Hochreiter S and Schmidhuber J 1999 Feature extraction through LOCOCODE Neural Comput. 11 679-714
12. Jutten C and Heute;rault J 1991 Blind separation of sources, part 1: an adaptive algorithm based on neuromimetic architecture Signal Process. 24 1-10
13. Lindstädt S 1993 Comparison of two unsupervised neural network models for redundancy reduction Proc. 1993 Connectionist Models Summer School ed M C Mozer et al (Hillsdale, NJ: Erlbaum) pp 308-15
14. Linsker R 1988 Self-organization in a perceptual network IEEE Comput. 21 105-17
15. Miller K D 1994 A model for the development of simple cell receptive fields and the ordered arrangement of orientation columns through activity-dependent competition between on-and off-centre inputs J. Neurosci. 14 409-41
16. Molgedey L and Schuster HG 1994 Separation of independent signals using time-delayed correlations Phys. Rev. Lett. 72 3634-7
17. Nadal J-P and Parga N 1997 Redundancy reduction and independent component analysis: conditions on cumulants and adaptive approaches Neural Comput. 9 1421-56
18. Parra L, Deco G and Miesbach S 1996 Statistical independence and novelty detection with information preserving nonlinear maps Neural Comput. 8 260-9
19. Rubner J and Schulten K 1990 Development of feature detectors by self-organization: a network model Biol. Cybern. 62 193-9
20. Rubner J and Tavan P 1989 A self-organization network for principal-component analysis Europhys. Lett. 10 693-8
21. Schmidhuber J H 1992 Learning factorial codes by predictability minimization Neural Comput. 4 863-79
22. _ 1993 Netzwerkarchitekturen, Zielfunktionen und Kettenregel Habilitationsschrift Institut für Informatik, Technische Universität München
23. _ 1999 Neural predictors for detecting and removing redundant information Adaptive Behavior and Learning ed H Cruse, J Dean and H Ritter (Dordrecht: Kluwer) in press
24. Schmidhuber J H and Prelinger D 1993 Discovering predictable classifications Neural Comput. 5 625-35
25. Schmidhuber J H, Eldracher M and Foltin B 1996 Semilinear predictability minimization produces well-known feature detectors Neural Comput. 8 773-86
26. Schraudolph N N 1995 Optimization of entropy with neural networks PhD Thesis University of California, San Diego
27. _ 1999 Local gain adaptation in stochastic gradient descent Proc. 9th Int. Conf. on Artificial Neural Networks (London: IEE) in press
28. Schraudolph N N and Sejnowski T J 1993 Unsupervised discrimination of clustered data via optimization of binary information gain Advances in Neural Information Processing Systems 5 ed S J Hanson et al (San Mateo, CA: Morgan Kaufmann) pp 499-506