Glossary Library

Technical GlossaryDeep Learning

Feature Hierarchy

The structure in which representations become increasingly abstract from lower to higher layers.

Feature hierarchy is one of the most powerful representational ideas in deep learning. Lower layers often learn simpler structures such as edges, textures, or local patterns, while higher layers move toward objects, concepts, or semantic structure. This multi-level representation logic enables deep networks to extract strong features automatically from raw data. It is one of the main foundations of model success in computer vision and language processing.

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Representation Learning

An approach in which informative, discriminative, and task-relevant internal representations are learned automatically from raw data.

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Non-negative Matrix Factorization

A dimensionality reduction technique that produces part-based and interpretable representations in non-negative data.

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Autoencoder-Based Dimensionality Reduction

An approach that learns lower-dimensional representations of data through neural-network-based compression.