Interpretation of MR images using self-organizing maps and knowledge-based expert systems
Digital Signal Processing
Scale-independent quality criteria for dimensionality reduction
Pattern Recognition Letters
Screening web breaks in a pressroom by soft computing
Applied Soft Computing
Relational generative topographic mapping
Neurocomputing
Questionnaire- versus voice-based screening for laryngeal disorders
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
SOMM – self-organized manifold mapping
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
A New Training Method for Large Self Organizing Maps
Neural Processing Letters
Self-Organizing Map Formation with a Selectively Refractory Neighborhood
Neural Processing Letters
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It is shown that a topographic product P, first introduced in nonlinear dynamics, is an appropriate measure of the preservation or violation of neighborhood relations. It is sensitive to large-scale violations of the neighborhood ordering, but does not account for neighborhood ordering distortions caused by varying areal magnification factors. A vanishing value of the topographic product indicates a perfect neighborhood preservation; negative (positive) values indicate a too small (too large) output space dimensionality. In a simple example of maps from a 2D input space onto 1D, 2D, and 3D output spaces, it is demonstrated how the topographic product picks the correct output space dimensionality. In a second example, 19D speech data are mapped onto various output spaces and it is found that a 3D output space (instead of 2D) seems to be optimally suited to the data. This is an agreement with a recent speech recognition experiment on the same data set