GTM: the generative topographic mapping
Neural Computation
A stochastic self-organizing map for proximity data
Neural Computation
Self-Organizing Maps
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
Self-organizing maps and clustering methods for matrix data
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
On the equivalence between kernel self-organising maps and self-organising mixture density networks
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Patch clustering for massive data sets
Neurocomputing
Topographic mapping of large dissimilarity data sets
Neural Computation
Relational generative topographic mapping
Neurocomputing
| Hi-index | 0.01 |
Topographic mapping offers an intuitive interface to inspect large quantities of electronic data. Recently, it has been extended to data described by general dissimilarities rather than Euclidean vectors. Unlike its Euclidean counterpart, the technique has quadratic time complexity due to the underlying quadratic dissimilarity matrix. Thus, it is infeasible already for medium sized data sets. We introduce two approximation techniques which speed up the complexity to linear time algorithms: the Nyström approximation and patch processing, respectively. We evaluate the techniques on three examples from the biomedical domain.