We have a training set with 4 data points, as follows : X = (0, 0) => Y = 1 : X = (0, 1) => Y = -1 : X = (1, 0) => Y = -1 : X = (1, 1) => Y = 1. Notice that the data above is not linearly separable, hence the perceptron algorithm will not be able to learn a classifier that gives the correct prediction for all four above data points. Add a 3rd dimension to each of the extra input dimension so that the data becomes linearly separable:
a) Third value is equal to first value for each data
b) Third value is 1 for one data point, and 0 for other three
c) Third value is opposite of second value for each data point
d) None of the above
I cannot visualize how come just making one point on different plane than the rest of three, there exist a line/plane such that both the classes are separable.