Developer Guide

Contents

Min-max

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
1
p
), ... ,
x
n
= (
x
n
1
, … ,
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:
where
a
and
b
are the parameters of the algorithm.

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804