Latent space

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A latent space, also known as a latent feature space or embedding space, is an embedding of a set of items within a manifold in which items which resemble each other more closely are positioned closer to one another in the latent space. Position within the latent space can be viewed as being defined by a set of latent variables that emerge from the resemblances from the objects.

In most cases, the dimensionality of the latent space is chosen to be lower than the dimensionality of the feature space from which the data points are drawn, making the construction of a latent space an example of dimensionality reduction, which can also be viewed as a form of data compression or machine learning.

A number of algorithms exist to create latent space embeddings given a set of data items and a similarity function.

See also[]

• Induced topology
• Clustering algorithm
• Intrinsic dimension
• Latent semantic analysis
• Self-organizing map

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