DocumentCode :
1923806
Title :
Funtional vector quantization by neural maps
Author :
Villmann, Thomas ; Schleif, Frank-Michael
Author_Institution :
Dept. of Math., Univ. of Appl. Sci. Mittweida, Mittweida, Germany
fYear :
2009
fDate :
26-28 Aug. 2009
Firstpage :
1
Lastpage :
4
Abstract :
We propose the utilization of Sobolev-norms in unsupervised and supervised vector quantization for clustering and classification of functional data. Sobolev-norms differ from the usual Minkowski-norm by the incorporation of derivatives such that the functional shape is taken into account. This leads to a more appropriate modelling of functional data. As we figure out, the Sobolev-norm can easily plugged into prototype based adaptive vector quantization algorithms to process functional data adequately. We show for an example application in remote sensing data analysis that this methodology may lead to improved performance of the algorithms.
Keywords :
data analysis; image coding; learning (artificial intelligence); neural nets; pattern classification; pattern clustering; remote sensing; vector quantisation; Minkowski-norm; Sobolev-norms utilization; adaptive vector quantization algorithms; functional data classification; functional data clustering; funtional vector quantization; neural maps; neural network quantizer; remote sensing data analysis; satellite remote sensing image analysis; supervised vector quantization; unsupervised vector quantization; Clustering algorithms; Data analysis; Machine learning; Mathematics; Prototypes; Remote sensing; Shape; Supervised learning; Training data; Vector quantization; Sobolev-norms; classification; vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS '09. First Workshop on
Conference_Location :
Grenoble
Print_ISBN :
978-1-4244-4686-5
Electronic_ISBN :
978-1-4244-4687-2
Type :
conf
DOI :
10.1109/WHISPERS.2009.5289064
Filename :
5289064
Link To Document :
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