= 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 <feature (machine learning)>[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.<Deep Learning by Ian Goodfellow and others>[Chapter 15, §15.4, distributed representations]{ref}
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