Suppose you have a very large feature vector X, used to predict a a vector of expected values y.
Is the sequential linear linear regression,
e.g.: coeff=regress(y, X);
followed by sequential feature reduction,
e.g. [coeff_subset] = sequentialfs(fun, X, y, 'direction', 'backward'); % where: fun = @(XT,yT,Xt,yt)(rmse(regress(yT, XT)'*Xt')', yt);
the easiest/best approach to get the a reasonable sized feature vecture when no other information is known?
It seems that, from my testing, this method rarely captures the features that matter the most, and I obtained better results by randomly selecting some of the features.
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