Infomax is an optimization principle for artificial neural networks and other information processing systems. It prescribes that a function that maps a set of input values I to a set of output values O should be chosen or learned so as to maximize the average Shannon mutual information between I and O, subject to a set of specified constraints and/or noise processes. Infomax algorithms are learning algorithms that perform this optimization process. The principle was described by Linsker in 1988.[1]
Infomax, in its zero-noise limit, is related to the principle of redundancy reduction proposed for biological sensory processing by Horace Barlow in 1961,[2] and applied quantitatively to retinal processing by Atick and Redlich.[3]
One of the applications of infomax has been to an independent component analysis algorithm that finds independent signals by maximizing entropy. Infomax-based ICA was described by Bell and Sejnowski, and Nadal and Parga in 1995.[4] [5]
See also
References
- ^ Linsker R (1988). "Self-organization in a perceptual network". IEEE Computer. 21 (3): 105–17. doi:10.1109/2.36. S2CID 1527671.
- ^ Barlow, H. (1961). "Possible principles underlying the transformations of sensory messages". In Rosenblith, W. (ed.). Sensory Communication. Cambridge MA: MIT Press. pp. 217–234.
- ^ Atick JJ, Redlich AN (1992). "What does the retina know about natural scenes?". Neural Computation. 4 (2): 196–210. doi:10.1162/neco.1992.4.2.196. S2CID 17515861.
- ^ Bell AJ, Sejnowski TJ (November 1995). "An information-maximization approach to blind separation and blind deconvolution". Neural Comput. 7 (6): 1129–59. CiteSeerX 10.1.1.36.6605. doi:10.1162/neco.1995.7.6.1129. PMID 7584893. S2CID 1701422.
- ^ Nadal J.P., Parga N. (1999). "Sensory coding: information maximization and redundancy reduction". In Burdet, G.; Combe, P.; Parodi, O. (eds.). Neural Information Processing. World Scientific Series in Mathematical Biology and Medicine. Vol. 7. Singapore: World Scientific. pp. 164–171.
- Bell AJ, Sejnowski TJ (December 1997). "The "Independent Components" of Natural Scenes are Edge Filters". Vision Res. 37 (23): 3327–38. doi:10.1016/S0042-6989(97)00121-1. PMC 2882863. PMID 9425547.
- Linsker R (1997). "A local learning rule that enables information maximization for arbitrary input distributions". Neural Computation. 9 (8): 1661–65. doi:10.1162/neco.1997.9.8.1661. S2CID 42857188.
- Stone, J. V. (2004). Independent Component Analysis: A tutorial introduction. Cambridge MA: MIT Press. ISBN 978-0-262-69315-8.
 | This applied mathematics-related article is a stub. You can help Wikipedia by expanding it. |
This page was last edited on 13 December 2020, at 02:25