Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Self-organizing maps
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Two efficient connectionist schemes for structure preserving dimensionality reduction
IEEE Transactions on Neural Networks
Interactive visualization and analysis of hierarchical neural projections for data mining
IEEE Transactions on Neural Networks
Visualization and self-organization of multidimensional data through equalized orthogonal mapping
IEEE Transactions on Neural Networks
A new model of self-organizing neural networks and its application in data projection
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
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A piecewise linear projection algorithm, based on kohonen's Self-Organizing Map, is presented. Using this new algorithm, neural network is able to adapt its neural weights to accommodate with input space, while obtaining reduced 2-dimensional subspaces at each neural node. After completion of learning process, first project input data into their corresponding 2-D subspaces, then project all data in the 2-D subspaces into a reference 2-D subspace defined by a reference neural node. By piecewise linear projection, we can more easily deal with large data sets than other projection algorithms like Sammon's nonlinear mapping (NLM). There is no need to re-compute all the input data to interpolate new input data to the 2-D output space.