Feature Screening

Principal Component Analysis (PCA) is a key technique in unsupervised learning widely used in machine learning and statistics to analyze data and reduce data dimensionality. Its core idea is to break down the original data into representative principal components, achieving dimensionality reduction and providing a new description of the data.