# In the machine learning literature, what does it mean to say that something is “embedded” in some space?

In the machine learning literature, I often see it said that something is "embedded" in some space. For instance, that something is "embedded" in feature space, or that our data are "embedded" in dot product space, etc. However, I've never actually seen an explanation of what this is supposed to mean. So what does it actually mean to say that something is "embedded" in some space?

• Your question is different, but the answer can be found here. I would wait for someone to provide an interesting answer to your question though. – nbro Dec 30 '20 at 20:04
• @nbro Ahh, yes, I seem to have missed that question. Thanks for that. – The Pointer Dec 31 '20 at 12:21

Embedding is the process of representing data (from a source domain) in a new (or target) domain. Usually, the source domain is discrete, and the target domain is continuous. For example, embedding words into the continuous vector space can be done by the word2vec method.

The main reason behind using the embedding is doing meaningful mathematical computations in the target domain, which is not possible or straightforward in the source domain. For example, summing two words "brother" - "man" + "woman" not meaningful in the word and character levels. However, when using word2vec, embedding("brother") - embedding("man") + embedding("woman") can be meaningful and comparable with other embedded vectors; It should be near the embedded vector of "sister".

• While this is of course correct, I'd add that also more abstract things like manifolds can be said to exist in a higher dimensional embedding space. In that case, one refers to an arbitrary vector space as the embedding space (as well), but wants to express that the data under consideration lives/varies (meaningfully) essentially only along a subset of the dimensions used to represent the data, where this subset of dimensions is called Manifold. Also the manifold is embedded in a (higher dimensional) space. – Daniel B. Jan 1 at 2:20