GTM: the generative topographic mapping
Neural Computation
Kernel-based equiprobabilistic topographic map formation
Neural Computation
Dynamic topology representing networks
Neural Networks
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel-based topographic map formation by local density modeling
Neural Computation
Clustering based on conditional distributions in an auxiliary space
Neural Computation
Joint entropy maximization in kernel-based topographic maps
Neural Computation
Self-Organization of Topographic Mixture Networks Using Attentional Feedback
Neural Computation
Reclassification as Supervised Clustering
Neural Computation
Neural Computation
Data Mining
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Ischemia detection with a self-organizing map supplemented by supervised learning
IEEE Transactions on Neural Networks
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Self-Organized Maps (SOMs) are a popular approach for clustering data. However, most SOM based approaches ignore prior knowledge about potential categories. Also, Self Organized Map (SOM) based approaches usually develop topographic maps with disjoint and uniform activation regions that correspond to a hard clustering of the patterns at their nodes. We present a novel Self-Organizing map, the Kernel Supervised Dynamic Grid Self-Organized Map (KSDG-SOM). This model adapts its parameters in a kernel space. Gaussian kernels are used and their mean and variance components are adapted in order to optimize the fitness to the input density. The KSDG-SOM also grows dynamically up to a size defined with statistical criteria. It is capable of incorporating a priori information for the known categories. This information forms a supervised bias at the cluster formation and the model owns the potentiality of revising incorrect functional labels. The new method overcomes the main drawbacks of most of the existing clustering methods that lack a mechanism for dynamical extension on the basis of a balance between unsupervised and supervised drives.