A nonlinear discriminant algorithm for feature extraction and data classification
- 1 January 1998
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 9 (6) , 1370-1376
- https://doi.org/10.1109/72.728388
Abstract
Presents a nonlinear supervised feature extraction algorithm that combines Fisher's criterion function with a preliminary perceptron-like nonlinear projection of vectors in pattern space. Its main motivation is to combine the approximation properties of multilayer perceptrons (MLPs) with the target free nature of Fisher's classical discriminant analysis. In fact, although MLPs provide good classifiers for many problems, there may be some situations, such as unequal class sizes with a high degree of pattern mixing among them, that may make difficult the construction of good MLP classifiers. In these instances, the features extracted by our procedure could be more effective. After the description of its construction and the analysis of its complexity, we illustrate its use over a synthetic problem with the above characteristicsKeywords
This publication has 6 references indexed in Scilit:
- Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networksPublished by Elsevier ,2003
- Artificial neural networks for feature extraction and multivariate data projectionIEEE Transactions on Neural Networks, 1995
- Neural Network Classifiers Estimate Bayesian a posteriori ProbabilitiesNeural Computation, 1991
- Optimized feature extraction and the Bayes decision in feed-forward classifier networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1991
- On the relations between discriminant analysis and multilayer perceptronsNeural Networks, 1991
- The optimised internal representation of multilayer classifier networks performs nonlinear discriminant analysisNeural Networks, 1990