Data: [1:n,1:d] data matrix of n cases and d features
Centered: centered data around zero by median if TRUE
Capped: TRUE: outliers are capped above 1 or below -1 and set to 1 or -1.
na.rm: If TRUE, infinite vlaues are disregarded
WithBackTransformation: If in the case for forecasting with neural networks a backtransformation is required, this parameter can be set to 'TRUE'.
pmin: defines outliers on the lower end of scale
pmax: defines outliers on the higher end of scale
Details
Normalizes features either between -1 to 1 (Centered=TRUE) or 0-1 (Centered=TRUE) without changing the distribution of a feature itself. For a more precise description please read [Thrun, 2018, p.17].
"[The] scaling of the inputs determines the effective scaling of the weights in the last layer of a MLP with BP neural netowrk, it can have a large effect on the quality of the final solution. At the outset it is besto to standardize all inputs to have mean zero and standard deviation 1 [(or at least the range under 1)]. This ensures all inputs are treated equally in the regularization prozess, and allows to choose a meaningful range for the random starting weights."[Friedman et al., 2012]
Returns
if WithBackTransformation=FALSE: TransformedData[1:n,1:d] i.e., normalized data matrix of n cases and d features
if WithBackTransformation=TRUE: List with - TransformedData: [1:n,1:d] normalized data matrix of n cases and d features
MinX: [1:d] numerical vector used for manual back-transformation of each feature
MaxX: [1:d] numerical vector used for manual back-transformation of each feature
Denom: [1:d] numerical vector used for manual back-transformation of each feature
Center: [1:d] numerical vector used for manual back-transformation of each feature
References
[Milligan/Cooper, 1988] Milligan, G. W., & Cooper, M. C.: A study of standardization of variables in cluster analysis, Journal of Classification, Vol. 5(2), pp. 181-204. 1988.
[Friedman et al., 2012] Friedman, J., Hastie, T., & Tibshirani, R.: The Elements of Statistical Learning, (Second ed. Vol. 1), Springer series in statistics New York, NY, USA:, ISBN, 2012.
[Thrun, 2018] Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, doctoral dissertation 2017, Springer, Heidelberg, ISBN: 978-3-658-20539-3, tools:::Rd_expr_doi("10.1007/978-3-658-20540-9") , 2018.