A fusion method for multi-valued data

TL;DR

This paper introduces a new deviation-based aggregation method for multi-valued data, aiming to enhance accuracy and reduce computational complexity in applications like image processing, deep learning, and decision making.

Contribution

It extends deviation-based aggregation functions to multidimensional data, improving upon existing methods and lowering temporal complexity for practical applications.

Findings

Favorable results in image processing tasks

Effective in deep learning applications

Reduces computational complexity in data aggregation

Abstract

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.