Euclidian Norm

Euclidian norm or $l^{2}$ norm is the square root of sum of all the squared vector values

| | A | |_{2} = \sqrt{\sum_{i} A_{i}^{2}}

The $l^{2}$ norm of a Vector; Euclidian Distance is this norm of the difference. Compare Manhattan Distance ( $l^{1}$ ); the $l^{2}$ penalty appears in L2 or Ridge Regression.

Related Notes