Hierarchical Feature Extraction is a process used in machine learning, particularly deep learning, where complex data representations are built through multiple layers of abstraction. It leverages layered neural network architectures to automatically learn and extract features from raw data, progressively assembling simple patterns into increasingly complex structures to improve model performance.