  Cellular Automata are discrete dynamical systems that evolve following simple
and local rules. Despite of its local simplicity, knowledge discovery in CA is
a NP problem. This is the main motivation for using data mining techniques for
CA study. The Principal Component Analysis (PCA) is a useful tool for data
mining because it provides a compact and optimal description of data sets. Such
feature have been explored to compute the best subspace which maximizes the
projection of the I/O patterns of CA onto the principal axis. The stability of
the principal components against the input patterns is the main result of this
approach. In this paper we perform such analysis but in the presence of noise
which randomly reverses the CA output values with probability $p$. As expected,
the number of principal components increases when the pattern size is
increased. However, it seems to remain stable when the pattern size is
unchanged but the noise intensity gets larger. We describe our experiments and
point out further works using KL transform theory and parameter sensitivity
analysis.
