Conference paper

Appeared in: ESANN 2011, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Editor list: Verleysen, Michel

Publication year: 2011

Pages: 105-110

ISBN: 978-2-87419-044-5

URL: https://www.esann.org/sites/default/files/proceedings/legacy/es2011-20.pdf

Conference: 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Abstract: 

A general method is presented for the assessment of data attribute variability, which plays an important role in initial screening of multi- and high-dimensional data sets. Instead of the commonly used second centralized moment, known as variance, the proposed method allows a mathematically rigorous characterization of attribute sensitivity given not only Euclidean distances but partial data comparisons by general similarity measures. Depending on the choice of measure different spectral features get highlighted by attribute assessment, this way creating new image segmentation aspects, as shown in a comparison of Euclidean distance, Pearson correlation and -divergence applied to multi-spectral images.

Harvard Citation style: Strickert, M., Labitzke, B., Kolb, A. and Villmann, T. (2011) Multispectral image characterization by partial generalized covariance, in Verleysen, M. (ed.) ESANN 2011, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco. pp. 105-110. https://www.esann.org/sites/default/files/proceedings/legacy/es2011-20.pdf

APA Citation style: Strickert, M., Labitzke, B., Kolb, A., & Villmann, T. (2011). Multispectral image characterization by partial generalized covariance. In Verleysen, M. (Ed.), ESANN 2011, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. (pp. 105-110). Ciaco. https://www.esann.org/sites/default/files/proceedings/legacy/es2011-20.pdf