Min-max normalization is an algorithm to linearly scale the observations by each feature (column) into the range [a, b].

Problem Statement

Given a set X of n feature vectors x 1 = (x 11 , … , x 1p ), ... , x n = (x n1 , … , x np ) of dimension p, the problem is to compute the matrix Y = (y i j ) n x p where the j-th column (Y ) j = (y i j ) i= 1, ..., n is obtained as a result of normalizing the column (X ) j = (x i j ) i= 1, ..., n of the original matrix as:


a and b are the parameters of the algorithm.

For more complete information about compiler optimizations, see our Optimization Notice.
Select sticky button color: 
Orange (only for download buttons)