Probabilistic Topic Maps: Navigating through Large Text Collections
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
A unified framework for model-based clustering
The Journal of Machine Learning Research
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Adaptive filtering with the self-organizing map: a performance comparison
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
ProbMap -- A probabilistic approach for mapping large document collections
Intelligent Data Analysis
Performance analysis of mobile communication networks with clustering and neural modelling
International Journal of Mobile Network Design and Innovation
Topographic mapping of large dissimilarity data sets
Neural Computation
Efficient pipelined architecture for competitive learning
Journal of Parallel and Distributed Computing
Performance evaluation of multi-service UMTS core networks with clustering and neural modelling
International Journal of Mobile Network Design and Innovation
SOM and neural gas as graduated nonconvexity algorithms
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
Warped K-Means: An algorithm to cluster sequentially-distributed data
Information Sciences: an International Journal
| Hi-index | 35.68 |
The efficient representation and encoding of signals with limited resources, e.g., finite storage capacity and restricted transmission bandwidth, is a fundamental problem in technical as well as biological information processing systems. Typically, under realistic circumstances, the encoding and communication of messages has to deal with different sources of noise and disturbances. We propose a unifying approach to data compression by robust vector quantization, which explicitly deals with channel noise, bandwidth limitations, and random elimination of prototypes. The resulting algorithm is able to limit the detrimental effect of noise in a very general communication scenario. In addition, the presented model allows us to derive a novel competitive neural networks algorithm, which covers topology preserving feature maps, the so-called neural-gas algorithm, and the maximum entropy soft-max rule as special cases. Furthermore, continuation methods based on these noise models improve the codebook design by reducing the sensitivity to local minima. We show an exemplary application of the novel robust vector quantization algorithm to image compression for a teleconferencing system