Ridge Regression and Ridge Regression Kernel

Ridge Regression and Ridge Regression Kernel

Reference:
1. scikit-learn linear_model ridge regression
2. Machine learning for quantum mechanics in a nutshell Authors
3. sample plot ridge path code from #F* Pedregosa --

Ridge regression

Ridge regression addresses some of the problems of Ordinary Least Squares by imposing a penalty on the size of coefficients. The ridge coefficients minimize a penalized residual sum of squares:
\[\underset{w}{min} {\left\| Xw-y\right\|_2^{2}} + \lambda\left\|{w}\right\|_2^2\]
Here, \(\lambda \ge 0\) is a complexity parameter that controls the amount of shrinkage: the larger the value of \(\lambda\), the greater the amount of shrinkage and thus the coefficients become robust to collinearity. The figure below show the relationship between the \(\lambda\) and oscillations of the weights.

Ridge Regression Theory

the \(\tilde{x}\) meaning the test dataset, and \(x_i\) means the training data:
\[f(\tilde{x}) = \sum_{i=0}^n \alpha_i k(\tilde{x}, x)\]
Although the dimensionalty of \(\mathbf{Hilbert}\) space can be high, the solution lives in the finite span of the projected training data, enabling a finite representation. The corresponding convex optimization problems is:
\[\underset{\alpha \varepsilon R^{n}} {\mathrm{argmin}} \sum_{i=1}^n(f(x_i - y_i)^2 + \lambda\left \| f \right \|_{2}^{H} \]
\[\Leftrightarrow \underset{\alpha \varepsilon R^{n}} {\mathrm{argmin}} \left \langle K\alpha-y, K\alpha-y \right \rangle + \lambda \alpha^{T}K\alpha\]
Where \(\left \| f \right \|_{2}^{H}\) is the norm of \(f\) in \(\mathbf{Hilbert}\) space, the complexity of the linear ridge regression model in feature space, and \(K \varepsilon R^{n\times n}, K_{i, j}=k(x_i, x_j)\) is the kernel matrix between training sampels. As before, setting the gradient to 0 yeilds an analytic solution for the regression coefficients:
\[\alpha^{T}K^{2}\alpha - 2\alpha^{T}Ky\ + y^{T}y + \lambda\alpha^{T}K\alpha = 0 \Leftrightarrow K^{2}\alpha + \lambda K\alpha\]
\[\Leftrightarrow \alpha=(K+\lambda I)^{-1}y \]
where \(\lambda\) is a hyperparameter determining strength of regularization. The norm of the coefficient vector \(\mathbf\sigma\) is related to the smooothness and simpler models.

Figure below give a example of a KRR model with Gaussian kernel that demostates the role length-scale hyperparameter \(\sigma\). Although \(\sigma\) is directly related to the regularization term. But it's does control smoothness of the predictor, and effectively regularizes.

The figure above show that kernel ridge regression with Gaussian kernel and different length scales. We learn \(\cos(x)\), the KRR models(dasded lines).The regularization constant \(\lambda\) set to be \(10^{-14}\), a very small \(\mathbf{\sigma}\) fit the train set well but in-between error are very bigger, while a too large \(\sigma\) results in too close to linear model, with both high train error and prediction error.

From above description we can see all information about KRR model is contained in the matrix \(\mathbf K\) of kernel evaluations between training data. Similarly, all info required to predict new inputs \(\tilde{x}\) is contained in the kernel matrix of training set versus prediction data.

Kernel Ridge Regression

The regression coefficients \(\lambda\) are obtained by solving the linear system of equations \((\mathbf K + \lambda \mathbf I)\lambda=\mathbf y\), where \((\mathbf K + \lambda \mathbf I)\) is symmetric and strictly positive definite. To solve this equation we can use Cholesky decomposition \(\mathbf K + \lambda \mathbf I\), where \(\mathbf U\) is upper triangular. One then we break up the \(\mathbf{U^{T}U\lambda=y}\) equantion into 2 equations, the first is \(\mathbf{U^{T}\beta=y}\), and the other is \(\mathbf{U\lambda=\beta}\). Since \(U^{T}\) is lower triangular and \(\mathbf{U}\) is upper triangular, this requires only 2 striaghtforward passes over the data called forward and backward substitution, respectively. For \(\mathbf{U^{T}\beta=y}\), just like below:
\[\mathbf{U_{1, 1}^T \beta_1=y_1} \Leftrightarrow \beta_1=y1/u_{1,1}\]
\[\mathbf{U_{2, 1}^T \beta_1 + \mathbf{U_{2, 2}^T} \beta_1=y_1} \Leftrightarrow \beta_2=(y2 - u_{1, 2}\beta_1)/u_{2,2}\]
\[ ...\]
\[\sum_{j}^i \mathbf{U_{i, j}^T}\beta_j=y_i \Leftrightarrow \beta_i=(y_i-\sum_{j=1}^{i-1}u_{j,i}\beta_j)/u_{i,j}\]
Once the model is trained, then predictions can be made, and the prediction for a new input \(\mathbf{\tilde{x}}\) is the inner product between the vector of coefficients and the vector of corresponding kernel evaluations.

For a test datasets \(\mathbf{\tilde{X}} \varepsilon \Bbb{R^{n \times d}}\), the rows \(\mathbf{\tilde{x},...,\tilde{x_n}}\), and \(\mathbf{L} \varepsilon \Bbb{R^{n \times n}}\) is the kernel matrix of training versus prediction inputs. \(\mathbf{L_{i,j}=k(x_i, \tilde{x_j})}\)
This method has the same order of complexity than an Ordinary Least Squares. In the next notes I will introduce the detail of implementation about KRR.