Online computing of non-stationary distributions velocity fields by an accuracy controlled growing neural gas
| Autor: | Hervé Frezza-Buet |
|---|---|
| Přispěvatelé: | Georgia Tech Lorraine [Metz], Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-Georgia Institute of Technology [Atlanta]-CentraleSupélec-Ecole Nationale Supérieure des Arts et Metiers Metz-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2014 |
| Předmět: |
Neural gas
business.industry Cognitive Neuroscience Quantization (signal processing) Vector quantization Video processing [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] Online Systems Artificial Intelligence Computer Science::Multimedia Graph (abstract data type) [INFO]Computer Science [cs] Computer vision Vector field Neural Networks Computer Artificial intelligence Velocity field business Algorithm Algorithms Growing neural gas Mathematics |
| Zdroj: | Neural Networks Neural Networks, Elsevier, 2014, 60, pp.203-221. ⟨10.1016/j.neunet.2014.08.014⟩ |
| ISSN: | 0893-6080 |
| DOI: | 10.1016/j.neunet.2014.08.014 |
| Popis: | International audience; This paper presents a vector quantization process that can be applied online to a stream of inputs. It enables to set up and maintain a dynamical representation of the current information in the stream as a topology preserving graph of prototypical values, as well as a velocity field. The algorithm relies on the formulation of the accuracy of the quantization process, that allows for both the updating of the number of prototypes according to the stream evolution and the stabilization of the representation from which velocities can be extracted. A video processing application is presented. |
| Databáze: | OpenAIRE |
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