Euclidean Distance
Norms quantify the size of a given vector. Distance tells you how far apart two vectors are.
- So really, in ML, you’re measuring the norm of a distance vector
Calculated using Pythagorean Theorem, where the Euclidean Distance is given by
The squared Euclidean Distance is just getting rid of the square root, i.e.
Machine Learning
This notation is used for Computer Vision, we call it the L2 Distance / L2 Norm Serendipity: This is also called the Root Mean Square, which I saw in ECE140.
Squared L2 Distace See Loss Function to see some discussions about why we would use L1 Distance vs L2 Distance.
Sum of Squared Differences (SSD)
This is the term that I saw from the Computer Vision world, learning from Cyrill Stachniss.