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I’m training a support-vector machine (SVM). Each training vector includes 2 features which are equal in magnitude and have opposite signs, i.e., F1 = -F2.
Is there any sense in doing so?
Is one of the features redundant and better to be removed?
Are the answers to the above dependent on the kernel being used? I’m currently using the Gaussian/radial-basis function (RBF) kernel.
There’s no sense in having both features; geometrically, they define a position on a single line, which can just as well be captured by a single feature. Remove one of them.