Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Self-organizing maps
GTM: the generative topographic mapping
Neural Computation
Kernel-based equiprobabilistic topographic map formation
Neural Computation
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Physical Models of Neural Networks
Physical Models of Neural Networks
Visual Explorations in Finance
Visual Explorations in Finance
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Yet another algorithm which can generate topography map
IEEE Transactions on Neural Networks
Asymptotic level density in topological feature maps
IEEE Transactions on Neural Networks
Journal of VLSI Signal Processing Systems
| Hi-index | 0.00 |
When using topographic maps for clustering purposes, which is now being considered in the data mining community, it is crucial that the maps are free of topological defects. Otherwise, a contiguous cluster could become split into separate clusters. We introduce a new algorithm for monitoring the degree of topology preservation of kernel-based maps during learning. The algorithm is applied to a real-world example concerned with the identification of 3 musical instruments and the notes played by them, in an unsupervised manner, by means of a hierarchical clustering analysis, starting from the music signal's spectrogram.