In the book Learning from Data written (by Abu Mostafa), we have the following exercise:
Let $\rho$ be minimum attainable from $y_n(W^{*T}X_n)$ where $W^*$ is the vector that separates the data. Show $\rho > 0$. Also assume the Perceptron Learning Algorithm is initialized with the 0 vector.
How to prove the above statement?
I thought that it could be negative since a Perceptron function returns either +/-1?
Even I wonder if I comprehend this proof question correctly.