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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 fordoi:10.1145/1163610.1163630 dblp:conf/pe-wasun/FloresERH06 fatcat:mrrcbjnomjdf7dtlyf6cjsx73i