normalize: Normalize training data

In accSDA: Accelerated Sparse Discriminant Analysis

Normalize training data

Description

Normalize a vector or matrix to zero mean and unit length columns.

Usage

normalize(X)

Arguments

X	a matrix with the training data with observations down the rows and variables in the columns.

Details

This function can e.g. be used for the training data in the ASDA function.

Value

normalize Returns a list with the following attributes:

Xc

The normalized data

mx

Mean of columns of X.

vx

Length of columns of X.

Id

Logical vector indicating which variables are included in X. If some of the columns have zero length they are omitted

Author(s)

Line Clemmensen

References

Clemmensen, L., Hastie, T. and Ersboell, K. (2008) "Sparse discriminant analysis", Technical report, IMM, Technical University of Denmark

See Also

normalizetest, predict.ASDA, ASDA

Examples

## Data
X<-matrix(sample(seq(3),12,replace=TRUE),nrow=3)

## Normalize data
Nm<-normalize(X)
print(Nm$Xc)

## See if any variables have been removed
which(!Nm$Id)

accSDA documentation built on Sept. 5, 2022, 5:05 p.m.