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Entity Embeddings of Categorical Variables
[article]
2016
arXiv
pre-print
We map categorical variables in a function approximation problem into Euclidean spaces, which are the entity embeddings of the categorical variables. The mapping is learned by a neural network during the standard supervised training process. Entity embedding not only reduces memory usage and speeds up neural networks compared with one-hot encoding, but more importantly by mapping similar values close to each other in the embedding space it reveals the intrinsic properties of the categorical
arXiv:1604.06737v1
fatcat:pvngy76mynbnljgfmilgbkjbty