Artificial neural network for nonlinear projection of multivariate data
- 2 January 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 3, 335-340
- https://doi.org/10.1109/ijcnn.1992.227152
Abstract
The authors propose a learning algorithm to train a multilayer feedforward neural network to perform the well-known Sammon nonlinear projection. The learning algorithm is an extension of the backpropagation algorithm. A significant advantage of the network-based projection over the original Sammon algorithm is that the trained network is able to project new patterns. Experimental results indicate that the projection network has good generalization capability when an appropriately sized training set and network are utilized. A lower bound for the number of free parameters required to achieve the same representation power as Shannon's algorithm is derived. This lower bound, together with the generalization capability, provides some guidelines about the size of the network that should be used.<>Keywords
This publication has 9 references indexed in Scilit:
- Projection pursuit learningPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Development of feature detectors by self-organizationBiological Cybernetics, 1990
- Approximation by superpositions of a sigmoidal functionMathematics of Control, Signals, and Systems, 1989
- A Self-Organizing Network for Principal-Component AnalysisEurophysics Letters, 1989
- Self-Organization and Associative MemoryPublished by Springer Nature ,1989
- Optimal unsupervised learning in a single-layer linear feedforward neural networkNeural Networks, 1989
- NEURAL NETWORKS, PRINCIPAL COMPONENTS, AND SUBSPACESInternational Journal of Neural Systems, 1989
- Evaluation of Projection AlgorithmsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1981
- A Nonlinear Mapping for Data Structure AnalysisIEEE Transactions on Computers, 1969