Catalogue of Artificial Intelligence Techniques
Keywords: euclidean, neighbour, vector
Author(s): Michael Clarke
Euclidean embeddings are a method of measuring ‘distances’ between data to group similar results and then run nearest neighbour type searches. Euclidean Embeddings are created by first mapping all the data in a 3D space according to properties of the results. Results with similar properties will be grouped together in certain dimensions of the map using Euclidean distances (distances along a straight line according to the displacement of each point). Result in similar locations can be mapped into groups (either multiple points in a single group or multiple points shared between multiple groups). If a point is then selected in one of these groups, other points in the group can also be used as alternatives. This is often used as a technique for bandwidth optimization by pre-grouping points to send and then sending an ID for the centre of the points. The Euclidean distances for all the other points within that group are then sent
- Golan, Euclidean embedding Approach, Thesis Paper.