An overview of mapping techniques for exploratory pattern analysis
Pattern Recognition
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Non-linear dimensionality reduction techniques for unsupervised feature extraction
Pattern Recognition Letters
A comparative study of neural network based feature extraction paradigms
Pattern Recognition Letters
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
A high performance k-NN classifier using a binary correlation matrix memory
Proceedings of the 1998 conference on Advances in neural information processing systems II
Statistical and neural classifiers: an integrated approach to design
Statistical and neural classifiers: an integrated approach to design
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
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New feature extraction method that handles nonlinearly separable datasets, preserves data geometry, minimises classification error directly and is designed especially for visualisation is suggested. The method employs hardware friendly binary correlation matrix memories (CMM), which makes the algorithm itself hardware friendly. To find coefficients of optimal linear orthogonal transformation and to speed up the calculations, binary CMM classifier and modified genetic optimisation technique are applied. The proposed technique was verified and compared with four competitive mapping techniques over a dozen of artificial and real world datasets. Experiments performed with respect to visualisation and classification accuracy showed that method is preferable to use on average sized nonlinear problems for extracting two features on behalf of visualisation.