Soft Margin SVM

In real-world applications, a margin does not always exist; that is, the classes are not always linearly separable in feature space by a hyperplane. The concept underlying the Soft Margin overcomes this limitation by introducing an additional variable $\xi$ for each sample, thereby relaxing (slackening) the margin constraint:

$$\begin{array}{l} y_i (\mathbf{w} \cdot \mathbf{x}_i + b) \ge 1 - \xi_i \\ \xi_i \ge 0, \forall i \end{array}$$ (5.37)

The parameter $\xi$ represents the slackness associated with the sample. When $0<\xi\le 1$, the sample is correctly classified but lies within the margin region. When $\xi>1$, the sample enters the decision space of the opposite class and is therefore misclassified.

The definition immediately implies that

$$\xi_i \ge \max \left( 0, 1-y_if(\mathbf{x}_i) \right)$$ (5.38)

At the optimum, the slack variables take the minimum feasible value, and therefore

$$\xi_i= \max \left( 0, 1-y_if(\mathbf{x}_i) \right).$$ (5.39)

To find a separating hyperplane that is in some sense optimal, the cost function to be minimized must also account for the distance between the sample and the margin:

$$\min \frac{1}{2} \Vert \mathbf{w} \Vert^2 + C \sum \xi_i$$ (5.40)

subject to constraints (5.37). The parameter $C$ is a degree of freedom that indicates how much a sample must pay for violating the margin constraint. When $C$ is small, the margin is wide, whereas when $C$ approaches infinity, the formulation reduces to the Hard Margin SVM formulation introduced earlier.

Each sample $\mathbf{x}_i$ can be in one of three possible states:

• it may lie beyond the margin $y_i(\mathbf{w}^\top \mathbf{x}_i + b)>1$ and consequently not contribute to the function;
• it may lie on or inside the margin and contribute to the solution as a support vector;
• it may finally lie inside the margin and be penalized in proportion to its deviation from the hard constraints.

The Lagrangian of system (5.40), with the constraints introduced by the variables $\xi$, is

$$\mathcal{L}(\mathbf{w},b,\xi,\alpha) = \frac{1}{2} \Vert\mat... ...} \cdot \mathbf{x}_i + b) - 1 + \xi_i) - \sum_i \gamma_i \xi_i$$ (5.41)

As the number of constraints increases, the dual variables are both $\bm{\alpha}$ and $\bm{\gamma}$.

The remarkable result is that, after applying the derivatives, the dual formulation of (5.41) becomes exactly identical to the dual formulation of the Hard Margin case: the variables $\xi_i$ do not appear in the dual formulation, and the only difference between the Hard Margin and Soft Margin cases lies in the constraint on the parameters $\alpha_i$, which in this case are bounded by

$$0 \le \alpha_i \le C$$ (5.42)

instead of the simple inequality $\alpha_i \ge 0$. The main advantage of this formulation is precisely the simplicity of the constraints and the fact that it reduces the Hard Margin case to a particular case ($C=\infty$) of the Soft Margin. The constant $C$ is an upper bound on the value that the $\alpha_i$ can take.