Enhanced Dimensionality Reduction in Time-Domain Optimization through PCA and Eigenvector Integration

Abstract

This study integrates “Principal-Component-Analysis (PCA)” with eigenvector integration techniques to provide a novel method for dimensionality reduction in time-domain optimization. Effective dimensionality reduction is increasingly hampered by the complexity of the data, which is essential for raising computing effectiveness and boosting model performance. “Principal-Component-Analysis (PCA)” is a significant tool in machine learning and data processing and is especially useful for high-resolution data. This study investigates the impact of “Principal-Component-Analysis (PCA)” on the performance and accuracy of three classification algorithms: Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN)) for medical image classification. Using data images of melanoma and eczema, Visual Geometry Group 16(VGG16) was used for feature extraction and then “Principal-Component-Analysis (PCA)” was used to reduce dimensionality. The results show that “Principal-Component-Analysis (PCA)” improves processing time and does not notably affect accuracy or other performance. The accuracy of the training model on PCA-reduced data is 99% (SVM), 98.75% (RF), and 98.75% (CNN), respectively, while the accuracy of the non-reduced data is 99.75%, 99.25%, and 99.75%, respectively. Additionally, the role of “Principal-Component-Analysis (PCA)” in accelerating the training process without compromising performance by shortening the training time is emphasized. This work highlights the importance of “Principal-Component-Analysis (PCA)” as the first step in ensuring fast and effective training of machine learning models while having a minimal effect on accuracy thus highlighting the importance of “Principal-Component-Analysis (PCA)” for high dimensional data while maintaining the accuracy and other performance measures with minimal negative effect and improved time complexity considerably.