Recently the problem of dimensionality reduction has received a lot of interests in many fields of information processing. We consider the case where data is sampled from a low d...
This paper describes and contrasts findings from two related projects where groups of science pupils investigated local air pollution using a collection of mobile sensors and devic...
Manifold learning can discover the structure of high dimensional data and provides understanding of multidimensional patterns by preserving the local geometric characteristics. Ho...
Collecting large consistent data sets for real world software projects is problematic. Therefore, we explore how little data are required before the predictor performance plateaus...
We present the Conformal Embedding Analysis (CEA) for feature extraction and dimensionality reduction. Incorporating both conformal mapping and discriminating analysis, CEA projec...