Optimising Data Processing in Industrial Settings: A Comparative Evaluation of Dimensionality Reduction Approaches

José Cação, Mário Antunes, José Santos, Miguel Monteiro

Abstract: The industrial landscape is undergoing a significant transformation marked by the integration of technology and manufacturing processes, giving rise to the concept of the Industrial Internet of Things (IIoT). IIoT is characterized by the convergence of manufacturing processes, smart IoT devices, and Machine Learning (ML) algorithms, enabling continuous monitoring and optimisation of industrial operations. However, this evolution translates into a substantial increase in the number of interconnected devices and the amount of generated data. Consequently, with ML algorithms facing an exponentially growing volume of data, their performance may decline, and processing times may significantly increase. Dimensionality reduction (DR) techniques emerge as a viable and promising solution, promoting dataset feature reduction and the elimination of irrelevant information. This paper presents a comparative study of various DR techniques applied to a real-world industrial use case, focusing on th