High-Level Feature Learning

High-level feature learning refers to a process where machine learning models, like deep neural networks, automatically identify complex patterns and representations from raw data. Instead of manually defining specific features, these models learn to recognize abstract and meaningful concepts—such as objects in an image or themes in text—by organizing layered information from simple to more sophisticated features. This enables the system to better understand and analyze data, improving tasks like recognition, classification, and prediction without extensive human intervention.