A fusion method for multi-valued data
This work addresses the need for efficient and effective data aggregation methods in time-sensitive domains, though it appears incremental as an extension of existing concepts.
The paper tackles the problem of aggregating multidimensional data by extending deviation-based aggregation functions, aiming to improve results over methods like penalty functions while reducing temporal complexity. It demonstrates applicability in image processing, deep learning, and decision-making with favorable results.
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.