Kernel PCA vs Linear PCA: Strengths and Limits
Principal Component Analysis (PCA) is one of the most widely used dimensionality reduction techniques in machine learning and data analysis. Its ability to compress high-dimensional data into fewer dimensions while retaining maximum variance makes it invaluable for visualization, noise reduction, and preprocessing. However, standard linear PCA has a fundamental limitation: it can only capture linear … Read more