To enable the user to visualize their own brain activity is an essential part of a Brain-Computer Interface (BCI). For visualization, we need a dimension reduction algorithm, because only a few relevant components of the high-dimensional feature vectors, extracted from the electroencephalogram (EEG) signals, can be visualized. For this purpose, three feature mapping methods, Principal Component...

An ink separation algorithm is introduced for printing with 6 to 9 inks. A spectral gamut mapping algorithm is also introduced that projects an input reflectance onto the manifold of the printer spectral gamut space The ink separation, which is finding the best ink combination to reproduce a given reflectance, is done by applying an interpolation between printer gamut points neighboring a proje...

Résumé. Un large panel de domaines d’application utilise des réseaux de capteurs géoréférencés pour mesurer divers évènements. Les séries temporelles fournies par ces réseaux peuvent être utilisées dans le but de dégager des connaissances sur les relations spatio-temporelles de l’activité mesurée. Dans cet article, nous proposons une méthode permettant d’abord de détecter des situations atypiqu...

This paper presents results from the application of dimensionality reduction algorithms to sensory-data time-series that were recorded from Robonaut – NASA’s humanoid robot – while it was being teleoperated through four tool manipulation tasks. The algorithms tested were Principal Component Analysis, Multidimensional Scaling, and Spatio-Temporal Isomap. Structures were shown to exist in some ca...

Previous work has shown that distance-similarity visualisation or ‘spatialisation’ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or ‘cluster growing’ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic due to the...

In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should gre...

Dimensionality reduction methods for visualization map the original high-dimensional data typically into two dimensions. Mapping preserves the important information of the data, and in order to be useful, fulfils the needs of a human observer. We have proposed a self-organizing map (SOM)based approach for visual surface inspection. The method provides the advantages of unsupervised learning and...