Efficient Machine Learning on Edge Computing Through Data Compression Techniques
Nerea Gómez Larrakoetxea, Joseba Eskubi Astobiza, Iker Pastor-López, Borja Sanz, Jon García-Barruetabeña, Agustín Zubillaga Rego
Abstract
This paper discusses the increasing amount of data handled by companies and the need to use <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Big Data</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Data Analytics</i> to extract value from this data. However, due to the large amount of data collected, challenges related to the computational capacity of machines often arise when performing this analysis to acquire relevant information for the organization, especially when we are using edge computing. The paper aims to train machine learning models using compressed data, with two compression techniques applied to the original data. The results show that models trained with compressed data achieved similar accuracy to those trained with uncompressed data, and different compression techniques were compared. The research extended a previous study by analyzing the use of autoencoders for compression and reducing both instances and dimensionality of the dataset. The accuracy rate of the models when trained with compressed data instead of original data was maintained.