A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Describing MANETS
2006
Proceedings of the 3rd ACM international workshop on Performance evaluation of wireless ad hoc, sensor and ubiquitous networks - PE-WASUN '06
Data collected in realistic mobility traces for mobile ad hoc networks (MANETS) is intrinsically high dimensional. Principal Component Analysis (PCA) is a good tool for reducing the data dimemsion by extracting important features of the data. We propose a method for computing principal components using iterative regression for high dimensional matricies with missing values with an application to node degree time series. We expand this method to handle an additional dimension of information for
doi:10.1145/1163610.1163630
dblp:conf/pe-wasun/FloresERH06
fatcat:mrrcbjnomjdf7dtlyf6cjsx73i