Computing feature matrices using PCA-SVD hybrid method on small-scale systems

This paper aims to develop an effective method for reduction and decomposition on large matrices with low required computational resources and fast processing times. Our contribution is to design a PCA-SVD hybrid method that dividesthe feature extraction into two phases: PCA-based size reduction and SVD-based decomposition. In our method, PCA is first applied to a large matrix to extract its important components