论文标题
自组织图，以探索部分观察到的数据和缺失值的归纳
Self-Organizing Maps for Exploration of Partially Observed Data and Imputation of Missing Values
论文作者
论文摘要
自组织图是一个无监督的神经网络，在化学计量学领域中广泛用于数据可视化和聚类。计算自组织图的经典Kohonen算法仅适用于没有任何缺失值的完整数据。但是，在许多应用中，部分观察到的数据是规范。在本文中，我们建议通过新标准扩展自组织地图，以不完整的数据，该标准还定义了丢失值的估计值。此外，还提供了名为Misssom的Kohonen算法的适应，以计算这些自组织图和算数缺失值。提供了有效的实施。对模拟数据和化学数据集进行的数值实验说明了MISSSOM的短计算时间，并评估了其在各种标准以及与最新情况相比的性能。
The self-organizing map is an unsupervised neural network which is widely used for data visualisation and clustering in the field of chemometrics. The classical Kohonen algorithm that computes self-organizing maps is suitable only for complete data without any missing values. However, in many applications, partially observed data are the norm. In this paper, we propose an extension of self-organizing maps to incomplete data via a new criterion that also defines estimators of the missing values. In addition, an adaptation of the Kohonen algorithm, named missSOM, is provided to compute these self-organizing maps and impute missing values. An efficient implementation is provided. Numerical experiments on simulated data and a chemical dataset illustrate the short computing time of missSOM and assess its performance regarding various criteria and in comparison to the state of the art.
