More than two classes: Logistic Regression

In the following, we are interested in the case of \(p\) classes with \(p>2\). After the previous discussion, it seems natural for the output to take the integer values \(y = 1, \dots, p\). However, it turns out to be helpful to use a different, so-called one-hot encoding. In this encoding, the output \(y\) is instead represented by the \(p\)-dimensional unit vector in \(y\) direction \(\mathbf{e}^{(y)}\),

(33)\[\begin{split} y \longrightarrow \mathbf{e}^{(y)} = \begin{bmatrix} e^{(y)}_1 \\ \vdots \\ e^{(y)}_y \\ \vdots \\ e^{(y)}_{p} \end{bmatrix} = \begin{bmatrix} 0 \\ \vdots \\ 1 \\ \vdots \\ 0 \end{bmatrix},\end{split}\]

where \(e^{(y)}_l = 1\) if \(l = y\) and zero for all other \(l=1,\ldots, p\). A main advantage of this encoding is that we are not forced to choose a potentially biasing ordering of the classes as we would when arranging them along the ray of integers.

A linear approach to this problem then again mirrors the case for linear regression. We fit a multi-variate linear model, Eq. (12), to the one-hot encoded dataset \(\lbrace(\mathbf{x}_{1}, \mathbf{e}^{(y_1)}), \dots, (\mathbf{x}_{m}, \mathbf{e}^{(y_m)})\rbrace\). By minimising the RSS, Eq. (10), we obtain the solution

\[\hat{\beta} = (\widetilde{X}^{T}\widetilde{X})^{-1} \widetilde{X}^{T} Y,\]

where \(Y\) is the \(m\) by \(p\) output matrix. The prediction given an input \(\mathbf{x}\) is then a \(p\)-dimensional vector \(\mathbf{f}(\mathbf{x}|\hat{\beta}) = \tilde{\mathbf{x}}^{T} \hat{\beta}\). On a generic input \(\mathbf{x}\), it is obvious that the components of this prediction vector would be real valued, rather than being one of the one-hot basis vectors. To obtain a class prediction \(F(\mathbf{x}|\hat{\beta}) = 1, \dots, p\), we simply take the index of the largest component of that vector, i.e.,

\[F(\mathbf{x}|\hat{\beta}) = \textrm{argmax}_{k} f_{k}(\mathbf{x}|\hat{\beta}).\]

The \(\textrm{argmax}\) function is a non-linear function and is a first example of what is referred to as activation function.

For numerical minimization, it is better to use a smooth activation function. Such an activation function is given by the softmax function

\[F_k(\mathbf{x}|\hat{\beta})= \frac{e^{-f_k(\mathbf{x}|\hat{\beta})}}{\sum_{k'=1}^pe^{-f_{k'}(\mathbf{x}|\hat{\beta})}}.\]

Importantly, the output of the softmax function is a probability \(P(y = k|\mathbf{x})\), since \(\sum_k F_k(\mathbf{x}|\hat{\beta}) = 1\). This extended linear model is referred to as logistic regression 1.

The current linear approach based on classification of one-hot encoded data generally works poorly when there are more than two classes. We will see in the next chapter that relatively straightforward non-linear extensions of this approach can lead to much better results.


Note that the softmax function for two classes is the logistic function.