A Principal Component Analysis Algorithm Based on Dimension Reduction Window
A Principal Component Analysis Algorithm Based on Dimension Reduction Window is a scholarly work, published in 2018 in ''IEEE Access''. The main subjects of the publication include covariance matrix, dimensionality reduction, data mining, artificial intelligence, computer science, facial recognition system, artificial neural network, graph dimension, chemometrics, principal component analysis, mathematics, pattern recognition, cluster analysis, and algorithm. The authors develop a novel algorithm named DRWPCA in this paper, it does not need to map the original data to the space of other dimensions for processing, but realizes the dimension reduction by analyzing the correlation between the dimensions, and therefore the physical meaning of the original data set is retained.