Recall the standard support vector machine formulation: minimize w^2_2 subject to yi(w xi w0) 1, i = 1, . . . , m, where m is the number of points and n is the number of dimensions, is a quadratic program because the objective function is quadratic and the constraints are affine. A linear program on the other hand uses only affine objective function and constraints, and there are efficient polynomial-time algorithms for solving them. By replacing the 2- norm in the objective function with the 1-norm (x1 = Pn i=1 |xi |), we get minimize w_1 subject to yi(w xi w0) 1, i = 1, . . . , m. (3) Note that this is still not a linear program because the objective value contains absolute values, but we will convert this problem into an equivalent linear problem. (i) Suppose we know that the output y depends only on a few input variables (i.e. the optimal w is sparse). Would the 1-norm or 2-norm SVM make more sense? Justify your