embedding space
The d-dimensional vector space that features from a higher-dimensional vector space are mapped to.
Plain English Explanation
The d-dimensional vector space that features from a higher-dimensional vector space are mapped to. Embedding space is trained to capture structure that is meaningful for the intended application. The dot product of two embeddings is a measure of their similarity.
How is it used?
Practitioners refer to embedding space when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.