A linear boundary might be too simple to capture the class structure.
One way of getting a nonlinear decision boundary in the input space is to find a linear decision boundary in an expanded space (similar to polynomial regression.)
Thus, \({{\mathbf{x}_i}}\) is replaced by \(\phi({{\mathbf{x}_i}})\), where \(\phi\) is called a feature mapping