Competitive learning algorithms for vector quantization
Neural Networks
Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Self-Organizing Maps
Vector Quantization and Projection Neural Network
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Robust non-linear dimensionality reduction using successive 1-dimensional Laplacian Eigenmaps
Proceedings of the 24th international conference on Machine learning
A robust nonlinear projection method using the neural gas network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Topology representing network map: a new tool for visualization of high-dimensional data
Transactions on computational science I
Perspectives of self-adapted self-organizing clustering in organic computing
BioADIT'06 Proceedings of the Second international conference on Biologically Inspired Approaches to Advanced Information Technology
Nonlinear projection using geodesic distances and the neural gas network
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Visualizing the quality of dimensionality reduction
Neurocomputing
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Isotopis a new neural method for nonlinear projection of high-dimensional data. Isotop builds the mapping between the data space and a projection space by means of topology preservation. Actually, the topology of the data to be projected is approximated by the use of neighborhoods between the neural units. Isotop is provided with a piecewise linear interpolator for the projection of generalization data after learning. Experiments on artificial and real data sets show the advantages of Isotop.