The most important of I inputs in "I"nput matrices with I rows and N columns depends on the "O"utput target matrix with O rows and N columns.
If input and output variables are separately standardized to zero-mean/unit-variance variables, inputs can be ranked more easily via stepwise selection algorithms. The most common of these are forward selection and backward selection. However, there are also combined forward/backward and backward/forward algorithms.
I tend to rely on the stepwise selection algorithms for linear transformations. These are available from the MATLAB function STEPWISEFIT.
A command line search yields
>> lookfor stepwise
addedvarplot - Create added-variable plot for stepwise regression
stepwise - Interactive tool for stepwise regression.
stepwisefit - Fit regression model using stepwise regression
stepwiseglm - Create generalized linear regression model by stepwise regression.
stepwiselm - Create 1st order linear regression model by stepwise regression.
The help and doc commands yield explanations and examples.
Another possibility is to use a 2nd order linear regression model by adding crossproducts and squares.
Most likely there are more sophisticated algorithms available. However, I have never had the need.
Hope this helps.
Thank you for formally accepting my answer
Greg
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