 Distributed representation

A distributed representation encodes an object through a pattern across several components, with each component reusable across many objects. In contrast to allocating a separate indicator to every object, shared components let related objects share information. For example, three binary attributes can encode eight combinations even though there are only three coordinates.

In machine learning, the coordinates are often learned real-valued features rather than named attributes. A word embedding can encode several aspects of word usage in one vector; no single coordinate needs to have a simple human interpretation. Combinatorial capacity alone does not guarantee useful learning: the training objective must make the representation's geometry relevant to the task.^[ref]