Kernel Principal Component Analysis (KPCA) is an extension of Principal Component Analysis (PCA) that allows for the analysis of non-linear data. While PCA identifies the directions of maximum variance in linear data, KPCA uses a kernel function to map the data into a higher-dimensional space, making it easier to uncover complex patterns. By applying KPCA, one can extract important features from data that may not be linearly separable. This technique is particularly useful in fields like image processing and bioinformatics, where data often exhibit intricate structures that traditional PCA cannot effectively analyze.