Dynamic ST-based PCA method for adaptive data detection

Abstract

This study developed a dynamic principal component analysis (PCA)-based algorithm for adaptive data detection. The algorithm employs suitable STs on the basis of various data to achieve high accuracy. The scree test (ST) has long been criticized for its subjectivity because no standard applies for retaining the correct number of components or factors when identifying various types of data. This article proposes a novel dynamic ST-based (STB) PCA method wherein a suitable ST is selected in using a support vector machine (SVM) for determining the correct number of components in data detection. The dynamic STB PCA can be employed as a solution to effectively detect various types of data. The proposed detection system can bridge the gap between input data and suitable STs for solving problems encountered when implementing data detection. The experimental results show that the STB PCA provides a ST-selection tool for automatically selecting the most suitable STs, and effectively detected various data using the STs. In the data detection, the proposed method outperforms existing PCA methods that do not consider suitable STs.