A Novel Measure for Quantifying the Topology Preservation of Self-Organizing Feature Maps
Neural Processing Letters
Piecewise Linear Projection Based on Self-Organizing Map
Neural Processing Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deriving meaningful rules from gene expression data for classification
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Community self-organizing map and its application to data extraction
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
PolSOM: A new method for multidimensional data visualization
Pattern Recognition
Visualizing asymmetric proximities with SOM and MDS models
Neurocomputing
Hi-index | 0.00 |
We propose two neural net based methods for structure preserving dimensionality reduction. Method 1 selects a small representative sample and applies Sammon's method to project it. This projected data set is then used to train a multilayer perceptron (MLP). Method 2 uses Kohonen's self-organizing feature map to generate a small set of prototypes which is then projected by Sammon's method. This projected data set is then used to train an MLP. Both schemes are quite effective in terms of computation time and quality of output, and both outperform methods of Jain and Mao (1992, 1995) on the data sets tried