In this paper we propose an extension of the notion of deviation-based aggregation function tailored to aggregate multidimensional data. Our objective is both to improve the results obtained by other methods that try to select the best aggregation function for a particular set of data, such as penalty functions, and to reduce the temporal complexity required by such approaches. We discuss how this notion can be defined and present three illustrative examples of the applicability of our new proposal in areas where temporal constraints can be strict, such as image processing, deep learning and decision making, obtaining favourable results in the process.

在本文中，我们提出了一种针对基于偏差的聚合函数概念的扩展，该概念专门针对聚合多维数据而设计。我们的目标是改善通过其他方法尝试为特定数据集选择最佳聚合函数（例如惩罚函数）的结果，并降低此类方法所需的时间复杂性。我们讨论了如何定义这个概念，并给出了三​​个新示例的示例性示例，这些示例说明了我们的新提议在时间限制严格的领域中的适用性，例如图像处理，深度学习和决策制定，并在此过程中获得了良好的结果。