# Why do we take +1. -1 for support vector hyperplane in SVM?

In the SVM, we have 3 hyperplanes,

1. one for separating positive class and negative class
2. The other two lying on the support vectors.

In the figure - The equation of hyperplanes lying on support vectors is given as

$$w.x + b = 1$$ and $$w.x + b = -1$$


Why do we choose +1 and -1 as their values, It means that from the decision boundary the hyperplanes lying on the support vectors have 1 unit distance (perpendicular from the x-axis). So the length of the margin is fixed. So how can we maximize it if the margin is fixed?

This is an excellent question and one I struggled with as well.

Firstly, the margin is not fixed. As your diagram shows, the margin $$m = \frac{2}{\lVert w \rVert}$$, which is a function of the 2-norm of the $$w$$ parameter, nothing else. So the margin is maximized by minimizing the norm of $$w$$.

But let's back up to see why.

We have some data represented as vectors $$x_i \in \mathbb{R}^n$$ and each $$x_i$$ is associated with a binary label $$y_i \in \{-1,1\}$$, for $$i \in \mathbb{N}$$. We could have made the labels anything, but choosing -1 and 1 is mathematically convenient.

An (affine) hyperplane is the generalization of a line in n-dimensional space defined as the set of points $$x$$ such that $$w^Tx + b = 0$$, where $$w, x \in \mathbb{R}^n$$ and $$w^Tx$$ is the dot (inner) product between these vectors. The choice of $$w$$ will change the orientation of the hyperplane and the choice of $$b$$ will determine its offset from the origin.

We want to find a hyperplane (i.e. choice of $$w, b$$) that separates the data $$x_i$$ according to their class labels. We assume our data can be perfectly separated by a line (or hyperplane), i.e. it is linearly separable. So we want a hyperplane such that when $$y_i = -1$$ then $$w^Tx_i + b \le 0$$ and when $$y_i = 1$$ then $$w^Tx_i + b \ge 0$$. There's actually a fairly straightforward algorithm called the perceptron algorithm that can find a hyperplane meeting those constraints.

But there are an infinite number of hyperplanes (i.e. choices of $$w, b$$) that can satisfy the constraints of separating the data classes, so in order for our hyperplane to work well in classifying future data, we want it to optimally separate the data classes such that it is not biased toward one class or another and is situated perfectly between them with maximal space on either side (maximal margins).

The easier way to set this up is that what we really want is to define two parallel hyperplanes, one just on the inside boundary of class $$y_i = -1$$ and the other just on the inside boundary of the class $$y_i = 1$$, then the actual decision boundary will be the parallel hyperplane exactly in the middle of these.

In other words, if we have our decision boundary hyperplane as $$w^Tx + b = 0$$, then we want to find a $$\delta > 0$$ such that we can define two parallel hyperplanes on either side: $$w^Tx + b = 0 \pm \delta$$. We'd like to maximize $$\delta$$ so that the distance between these two boundary hyperplanes is maximal, that will give us maximal separation between the classes.

But if we keep $$\delta$$ a variable, then changing the norm of $$w$$, changing $$b$$ or changing $$\delta$$ will change the margin between the two hyperplanes. We really only want to optimize $$w, b$$. Moreover, if we change $$\delta$$ by any scalar amount, then we can just scale $$w, b$$ an opposite amount, so we can fix $$\delta$$ to anything we want and still be able to adjust the margin by modifying $$w, b$$.

This is where the $$\pm 1$$ comes from, it is from arbitrarily fixing $$\delta = \pm 1$$ because it is a convenient choice.

So we define our two parallel separating hyperplanes as: $$w^Tx + b = \pm 1$$

Then the margin is the distance between these two parallel hyperplanes. The displacement from the origin to a hyperplane is $$\frac{b}{\Vert w \Vert}$$, and we can use this fact to compute the distance between our hyperplanes. The distance from the origin to the first hyperplane is $$\frac{b+1}{\Vert w \Vert}$$ and to the second hyperplane is $$\frac{b-1}{\Vert w \Vert}$$, so the distance between them is (aka margin $$m$$): $$m = \frac{b+1}{\Vert w \Vert} - \frac{b-1}{\Vert w \Vert}$$ $$m = \frac{2}{\Vert w \Vert}$$

So the optimal hyperplane will be found by maximizing $$m$$, i.e. $$\underset{w}{max}\frac{2}{\Vert w \Vert} = \underset{w}{min}\frac{1}{2}{\Vert w \Vert}$$ subject to the constraint $$y_i(w^Tx_i + b) \ge 1$$.

Now, if we had arbitrarily chosen $$\delta = \pi$$ instead of 1, the margin would be $$\frac{2\pi}{\Vert w \Vert}$$, but this is just a change in scaling and the optimization problem is identical in form.

• "if we change $\delta$ by any scalar amount, then we can just scale $w, b$ an opposite amount, so we can fix $\delta$ to anything we want" I'm not buying this. Can you elaborate on this scaling stuff and explain how it entails that you can fix $\delta$ to anything you want? Feb 16 at 10:27