Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 2
Min-max normalization is an algorithm to linearly scale the observations by each feature (column) into the range [a, b].
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: