Ridge Regression

Ridge Regression#

The ridge regression is regression with the \(L_2\) regularization, $\( \mathcal{L} = \sum_{i=1}^n L(y,\hat y) + \lambda|\theta|^2 \)$

  • \(\theta\) : The parameter vector with the bias parameter as zero instead of one \(\begin{bmatrix} 0 & \theta_1 & \ldots & \theta_d\end{bmatrix}\) to prevent penalizing the bias term.

For simplicity, here one out we will ignore the bias term.

There are very strong properties of ridge regression that makes this regularization favorable:

  1. Guarantees a unique solution because \(\mathcal L(\theta)\) is always convex even if \(L(\theta)\) is not (for least-square it is).

  2. Reduces overfitting by penalizing large parameters.

Positive Definite Normal Equation#

The addition of the \(L_2\) regularization guarantees that \(\mathcal L(\theta)\) is convex simply because the matrices in the normal equation are guaranteed to be positive definite.

Let’s take for example the least-squares loss, $\( \mathcal L(\theta) = \sum_{i=1}^n{|y - X\theta|^2 +\lambda|\theta|^2} \)\( The normal equation is given by, \)\( \begin{gather} (X^\top X + \lambda I)\theta = X^\top y\\ \theta^* = \frac{2|y-X\theta^*|^2}{\lambda} X^\top \left({X\theta^* - y}\right) \end{gather} \)$

Relationship to MAP#

The addition of the \(L_2\) regularization is equivalent of turning an MLE problem to an MAP problem (i.e., by adding a prior).

Once again we use the least-squares loss as an example. The least-squares loss is the log-likelihood function for the Gaussian likelihood. We simply attempt to write the log-posterior problem with this likelihood, $\( \begin{align} \theta^* &= \mathop{\arg\min}_\theta \sum_{i=1}^n|y + X\theta|^2 - \lambda |\theta|^2\\ \theta^* &= \mathop{\arg\max}_\theta \left[-\sum_{i=1}^n|y - X\theta|^2 - \lambda |\theta|^2\right] \end{align} \)\( Thus the prior \)P(\theta) \propto \lambda |\theta|^2$.

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