Multi-class classifier via matrix least squares. Consider the least squares multi-class classifier de- scribed in $14.3, with a regression model fr(x) = x Bk for the one-versus-others classifiers. (We assume that the offset term is included using a constant feature. ) Show that the coefficient vectors B1, . .., BK can be found by solving the matrix least squares problem of minimizing ||XT B - Y||2, where S is the n x K matrix with columns B1, ..., BK, and Y is an N x K matrix. (a) (10 points) Give Y, i.e., describe its entries. What is the ith row of Y?(b) (10 points) Assuming the rows of X (i.e., the data feature vectors) are linearly independent, show that the least squares estimate is given by 5 = (X T)TY