1. Kernel Space Transformation (20 points): Kernel functions are the functions that map one space to a higher dimensional space. This question demonstrates the primary use of kernel functions in SVM. Consider four points: A=(1,1),B=(2,2),C=(2,1), D=(1,2) and two labels red, blue. Suppose A, B belong to the label red and C, D belong to the label blue. (a) Are labels red and blue linearly separable? Explain your reasoning in words with visualization. (b) Consider the kernel function: (x)=(x2x1,x1x2,x1+x2) where x=(x1,x2). We can observe that this kernel function maps 2-dimensional space to 3-dimensional space. What are the points A,B,C, D after transformation? (c) Are labels red and blue linearly separable after the transformation? Explain your reasoning