Self-Organizing Maps
Kernel-based topographic map formation by local density modeling
Neural Computation
Self-organizing maps with recursive neighborhood adaptation
Neural Networks - New developments in self-organizing maps
Self-organizing map algorithm and distortion measure
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Advanced visualization of self-organizing maps with vector fields
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
TASOM: a new time adaptive self-organizing map
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The effect of concave and convex weight adjustments on self-organizing maps
IEEE Transactions on Neural Networks
Self-organizing feature maps with self-adjusting learning parameters
IEEE Transactions on Neural Networks
Centroid neural network for unsupervised competitive learning
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
The parameterless self-organizing map algorithm
IEEE Transactions on Neural Networks
Rival-Model Penalized Self-Organizing Map
IEEE Transactions on Neural Networks
Explicit Magnification Control of Self-Organizing Maps for “Forbidden” Data
IEEE Transactions on Neural Networks
Ranked Centroid Projection: A Data Visualization Approach With Self-Organizing Maps
IEEE Transactions on Neural Networks
An efficient neural network based method for medical image segmentation
Computers in Biology and Medicine
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In this paper, a training method for the formation of topology preserving maps is introduced. The proposed approach presents a sequential formulation of the self-organizing map (SOM), which is based on a new model of the neuron, or processing unit. Each neuron acts as a finite impulse response (FIR) system, and the coefficients of the filters are adaptively estimated during the sequential learning process, in order to minimize a distortion measure of the map. The proposed FIR-SOM model deals with static distributions and it computes an ordered set of centroids. Additionally, the FIR-SOM estimates the learning dynamic of each prototype using an adaptive FIR model. A noteworthy result is that the optimized coefficients of the FIR processes tend to represent a moving average filter, regardless of the underlying input distribution. The convergence of the resulting model is analyzed numerically and shows good properties with respect to the classic SOM and other unsupervised neural models. Finally, the optimal FIR coefficients are shown to be useful for visualizing the cluster densities.