Using PCA and ICA for exploratory data analysis in situation awareness
Johan Himberg, Jani Mäntyjärvi, Panu Korpipää · 2002
This paper presents an approach for analyzing hand held device usage situation (context) phenomena. The situation information under examination is multidimensional fuzzy feature information derived from multisensor measurements. The analysis is conducted using principal component analysis (PCA) and independent component analysis (ICA). PCA is used to fuse multidimensional feature information into a more compact representation while the ICA is applied to extract patterns containing independent low level information about the situation. The results show that a few principal components compress the situation data representation efficiently. In addition, principal component representation provides a method for visualizing high level situation information. Most independent components extracted from the usage situation data correlate strongly with some of the original signals. This suggests that the original context data already consist of relatively independent signals if the temporal relations in the data are omitted.