A Method for Extracting Features of Modern Folk Opera Performance Art Based on Principal Component Analysis

Abstract

With the continuous development of China’s economy and society and the gradual reform of various industries, the modern folk opera performance art has received more and more attention, and through the excavation of features in the folk opera performance art, the modern folk opera performance level can be promoted. This paper proposes a generalised principal component analysis (PCA) feature extraction method, which first reorganizes the image matrix, constructs the overall scatter matrix based on the reorganized image matrix, and then finds the best projection vector for feature extraction. The proposed method is a further extension of the 2DPCA module, which can build a scatter matrix of arbitrary dimensions and obtain a projection vector of arbitrary dimensions. The results show that the best feature extraction is achieved by optimising the SVM with a principal component contribution of 50% and using the grid search algorithm. The smaller the dimension of the scatter matrix, the stronger the feature extraction ability of the generalised principal component analysis and the faster the feature extraction speed.