缺失数据：神经网络和期望最大化技术的比较
Missing Data: A Comparison of Neural Network and Expectation Maximisation Techniques
摘要：
在实时处理应用中丢失的输入矢量元素的估计需要一个系统，该系统拥有某些特性的知识，例如变量之间的相关性，这些特性是输入空间中固有的。计算智能技术和极大似然技术确实具有这样的特征，因此对于缺失数据的填补是重要的。本文比较了两种处理缺失数据估计问题的方法。第一种技术是基于对该问题的现有技术方法，即使用极大似然（ML）和期望最大化（EM）。第二种方法是使用基于自联想神经网络和遗传算法的系统，如Adbella和Marwala3所讨论的。基于三个数据集对这两种方法的估计能力进行了比较，并得出了结论。
英文摘要：
The estimation of missing input vector elements in real time processing applications requires a system that possesses the knowledge of certain characteristics such as correlations between variables, which are inherent in the input space. Computational intelligence techniques and maximum likelihood techniques do possess such characteristics and as a result are important for imputation of missing data. This paper compares two approaches to the problem of missing data estimation. The first technique is based on the current state of the art approach to this problem, that being the use of Maximum Likelihood (ML) and Expectation Maximisation (EM. The second approach is the use of a system based on auto-associative neural networks and the Genetic Algorithm as discussed by Adbella and Marwala3. The estimation ability of both of these techniques is compared, based on three datasets and conclusions are made.
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。
