元胞自动机PCA模型挖掘
Mining Cellular Automata DataBases throug PCA Models
摘要：
元胞自动机是离散的动力系统，遵循简单的局部规则演化。尽管其局部简单，CA中的知识发现是一个NP问题。这是使用数据采掘技术进行CA研究的主要动机。主成分分析（PCA）是数据采掘的一个有用的工具，因为它提供了一个紧凑的和最佳的数据集的描述。这种功能已被探索计算最佳子空间，最大化的投影CA的I/O模式到主轴。主成分对输入模式的稳定性是这种方法的主要结果。在本文中，我们进行这样的分析，但在噪声的存在下，随机反转CA输出值的概率$p$。正如预期的那样，主成分的数量随着模式大小的增加而增加。然而，当图案尺寸不变但噪声强度变大时，它似乎保持稳定。我们描述了我们的实验，并指出进一步的工作，使用KL变换理论和参数敏感性分析。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
