The Random Subspace Coarse Coding Scheme for Real-valued Vectors
Two coarse coding schemes are considered: the random subspace scheme of the authors, and the modified Kanerva model of Prager et al. (1993). Some properties and characteristics of these schemes are investigated experimentally and by analysing their geometrical interpretation. Both schemes do not require exponential growth of the binary code dimensionality against that of the input space. The random subspace scheme allows the code density to be independent from the maximal dimensionality of hyper-rectangle receptive fields. It is especially important when low-dimensional receptive fields are required, as with classifiers or approximators of real-world data.
E. M. Kussul et al., "The Random Subspace Coarse Coding Scheme for Real-valued Vectors," Proceedings of the International Joint Conference on Neural Networks, vol. 1, pp. 450-455, Institute of Electrical and Electronics Engineers (IEEE), Jan 1999.
The definitive version is available at https://doi.org/10.1109/IJCNN.1999.831537
International Joint Conference on Neural Networks (IJCNN'99) (1999: Jul. 10-16, Washington, DC)
Electrical and Computer Engineering
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 1999 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 1999