MultiPLS 
For each Y that gets identified by the regular expression for Y attributes, the specified PLS (partial least squares) algorithm gets applied to the X attributes identified by the corresponding regular expression.

PLS 
Applies the specified partial least squares (PLS) algorithm to the data.

PLSFilterExtended 
Class contains changes to the Weka's PLSFilter in order to
have simpls work with multiple y attributes.

PLSFilterWithLoadings 
Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction.
By default it replaces missing values and centers the data.
Allows access to the internal matrices.
For more information see:
Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).

SIMPLSMatrixFilter 
Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction.
By default it replaces missing values and centers the data.
Allows access to the internal matrices.
For more information see:
Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).

SIMPLSMatrixFilterFromGeneticString 
Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction.
By default it replaces missing values and centers the data.
Allows access to the internal matrices.
For more information see:
Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).

YGradientEPO 
Applies the External Parameter Orthogonalization (EPO) algorithm to the data.
For more information see:
http://wiki.eigenvector.com/index.php?title=Advanced_Preprocessing:_Multivariate_Filtering#External_Parameter_Orthogonalization_.28EPO.29
Valid options are:

YGradientGLSW 
Applies the Generalized Least Squares Weighting (GLSW) algorithm to the data.
For more information see:
http://wiki.eigenvector.com/index.php?title=Advanced_Preprocessing:_Multivariate_Filtering#YGradient_GLSW
Valid options are:
