@article{ART003352918},
author={Hyun-Seob Lee},
title={A Principal Component Analysis Based Data Preprocessing Policy for Improving Machine Learning Performance in Low Specification Computing Environments},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2026},
volume={12},
number={3},
pages={1}
TY - JOUR
AU - Hyun-Seob Lee
TI - A Principal Component Analysis Based Data Preprocessing Policy for Improving Machine Learning Performance in Low Specification Computing Environments
JO - Journal of Internet of Things and Convergence
PY - 2026
VL - 12
IS - 3
PB - The Korea Internet of Things Society
SP - 1
EP -
SN - 2466-0078
AB - Research is actively underway to establish automated environments through the application of artificial intelligence across various fields. However, building high-specification computing environments for applying machine learning incurs significant costs. Consequently, the need for research on efficiently applying AI in low-specification environments has emerged. Machine learning on high-dimensional data in low-specification computing environments suffers severe performance degradation due to memory constraints and computational costs. This study proposes a dimension reduction technique utilizing Principal Component Analysis (PCA) to address these issues. The proposed method adds a preprocessing step using PCA before performing machine learning on high-dimensional data, enabling machine learning to be performed using the refined data. This simultaneously improves the performance and efficiency of machine learning models even in environments with limited computing resources. Experiments confirmed that PCA-based preprocessing effectively reduces the dimensions of multidimensional data while preserving key variance information under identical conditions. Consequently, average training time decreased by 96.6%, memory usage was reduced by 99.7%, and model prediction accuracy remained at a comparable level. This research demonstrates that efficient machine learning implementation is possible even in environments with limited hardware resources, such as embedded systems or edge computing. This is expected to provide a practical solution for researchers and developers with limited access to high-performance computing infrastructure.
KW - Principal Component Analysis;Machine Learning;Dimensionality Reduction;Low Specification Computing;Data Preprocessing
DO -
UR -
ER -
Hyun-Seob Lee. (2026). A Principal Component Analysis Based Data Preprocessing Policy for Improving Machine Learning Performance in Low Specification Computing Environments. Journal of Internet of Things and Convergence, 12(3), 1.
Hyun-Seob Lee. 2026, "A Principal Component Analysis Based Data Preprocessing Policy for Improving Machine Learning Performance in Low Specification Computing Environments", Journal of Internet of Things and Convergence, vol.12, no.3 1.
Hyun-Seob Lee "A Principal Component Analysis Based Data Preprocessing Policy for Improving Machine Learning Performance in Low Specification Computing Environments" Journal of Internet of Things and Convergence 12.3 1 (2026) : 1.
Hyun-Seob Lee. A Principal Component Analysis Based Data Preprocessing Policy for Improving Machine Learning Performance in Low Specification Computing Environments. 2026; 12(3), 1.
Hyun-Seob Lee. "A Principal Component Analysis Based Data Preprocessing Policy for Improving Machine Learning Performance in Low Specification Computing Environments" Journal of Internet of Things and Convergence 12, no.3 (2026) : 1.
Hyun-Seob Lee. A Principal Component Analysis Based Data Preprocessing Policy for Improving Machine Learning Performance in Low Specification Computing Environments. Journal of Internet of Things and Convergence, 12(3), 1.
Hyun-Seob Lee. A Principal Component Analysis Based Data Preprocessing Policy for Improving Machine Learning Performance in Low Specification Computing Environments. Journal of Internet of Things and Convergence. 2026; 12(3) 1.
Hyun-Seob Lee. A Principal Component Analysis Based Data Preprocessing Policy for Improving Machine Learning Performance in Low Specification Computing Environments. 2026; 12(3), 1.
Hyun-Seob Lee. "A Principal Component Analysis Based Data Preprocessing Policy for Improving Machine Learning Performance in Low Specification Computing Environments" Journal of Internet of Things and Convergence 12, no.3 (2026) : 1.
