Feature learning

Set of learning techniques in machine learning / From Wikipedia, the free encyclopedia

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In machine learning, feature learning or representation learning[2] is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task.

Diagram of the feature learning paradigm in machine learning for application to downstream tasks, which can be applied to either raw data such as images or text, or to an initial set of features for the data. Feature learning is intended to result in faster training or better performance in task-specific settings than if the data was inputted directly.[1]

Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensor data have not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.

Feature learning can be either supervised, unsupervised or self-supervised.

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