### Deriving Normal Equation of Linear Regression ModelTweet

Cost function has been explained in Week 1 and Week 2 of Machine Learning course taught by Andrew Ng. This post tries to explain how to derive normal equation for linear regression with multiple variables. It is a good thing if all readers has studied Week 1 and Week 2 before reading this post.

The cost function of linear regression with multiple variables, $J(\theta)$ is formulated as follows:

$$$J(\theta) = \frac{1}{2m} \sum_{i=1}^{m}{(h_{\theta}(x^{(i)}) - y^{(i)})^2} \tag{1}\label{eq:cost-function}$$$

with $m$ is number of instances in dataset, $h_{\theta}(x^{(i)})$ is our hyphotesis also known as prediction model for the $i$th instance, and $y^{(i)}$ is true value for the $i$th instance.

We also have studied that

$$$h_{\theta}(x^{(i)}) = \theta_0 + \theta_1 x_1^{(i)} + \cdots + \theta_n x_n^{(i)} \tag{2}\label{eq:the-hyphotesis}$$$

By substituting \eqref{eq:the-hyphotesis} into \eqref{eq:cost-function}, we obtain

\begin{align} J(\theta) &= \frac{1}{2m} \sum_{i=1}^{m}{(\theta_0 + \theta_1 x_1^{(i)} + \cdots + \theta_n x_n^{(i)} - y^{(i)})^2} \tag{3}\label{eq:derivation-1} \\ &= \frac{1}{2m} ((\theta_0 + \theta_1 x_1^{(1)} + \cdots + \theta_n x_n^{(1)} - y^{(1)} )^2 + \cdots + (\theta_0 + \theta_1 x_1^{(m)} + \cdots + \theta_n x_n^{(m)} - y^{(m)} )^2 ) \tag{4}\label{eq:derivation-2} \\ &= \frac{1}{2m} \underbrace{ \begin{bmatrix} (\theta_0 + \theta_1 x_1^{(1)} + \cdots + \theta_n x_n^{(1)} - y^{(1)}) & \cdots & (\theta_0 + \theta_1 x_1^{(m)} + \cdots + \theta_n x_n^{(m)} - y^{(m)}) \end{bmatrix}}_{\text{matrix with size: } 1 \times m} \underbrace{\begin{bmatrix} (\theta_0 +\theta_1 x_1^{(1)} + \cdots + \theta_n x_n^{(1)} - y^{(1)}) \\ \vdots \\ (\theta_0 +\theta_1 x_1^{(m)} + \cdots + \theta_n x_n^{(m)} - y^{(m)}) \end{bmatrix}}_{\text{matrix with size: } m \times 1} \tag{5}\label{eq:derivation-3} \\ &= \frac{1}{2m} \left( \begin{bmatrix} (\theta_0 + \theta_1 x_1^{(1)} + \cdots + \theta_n x_n^{(1)} ) & \cdots & (\theta_0 + \theta_1 x_1^{(m)} + \cdots + \theta_n x_n^{(m)} ) \end{bmatrix} - \begin{bmatrix} y^{(1)} & \cdots & y^{(m)} \end{bmatrix} \right) \left( \begin{bmatrix} \theta_0 + \theta_1 x_1^{(1)} + \cdots + \theta_n x_n^{(1)} \\ \theta_0 + \theta_1 x_1^{(2)} + \cdots + \theta_n x_n^{(2)} \\ \vdots \\ \theta_0 + \theta_1 x_1^{(m)} + \cdots + \theta_n x_n^{(m)} \end{bmatrix} - \begin{bmatrix} y^{(1)} \\ y^{(2)} \\ \vdots \\ y^{(m)} \end{bmatrix} \right) \tag{6}\label{eq:derivation-4} \\ &= \frac{1}{2m} \left( \begin{bmatrix} \theta_0 & \theta_1 & \cdots & \theta_n \end{bmatrix} \begin{bmatrix} 1 & 1 & \cdots & 1 \\ x_1^{(1)} & x_1^{(2)} & \cdots & x_1^{(m)} \\ \vdots & \vdots & \cdots & \vdots \\ x_n^{(1)} & x_n^{(2)} & \cdots & x_n^{(m)} \\\end{bmatrix} - \begin{bmatrix} y^{(1)} & \cdots & y^{(m)} \end{bmatrix} \right) \left( \begin{bmatrix} 1 & x_1^{(1)} & \cdots & x_n^{(1)} \\ 1 & x_1^{(2)} & \cdots & x_n^{(2)} \\ \vdots & \vdots & \cdots & \vdots \\ 1 & x_1^{(m)} & \cdots & x_n^{(m)} \end{bmatrix} \begin{bmatrix} \theta_0 \\ \theta_1 \\ \vdots \\ \theta_n \end{bmatrix} - \begin{bmatrix} y^{(1)} \\ y^{(2)} \\ \ldots \\ y^{(m)} \end{bmatrix} \right) \tag{7}\label{eq:derivation-5} \end{align}

By defining

$$$\theta = \begin{bmatrix} \theta_0 \\ \theta_1 \\ \vdots \\ \theta_n \end{bmatrix} \tag{8}\label{eq:defining-theta}$$$

and

$$$X = \begin{bmatrix} 1 & x_1^{(1)} & \cdots & x_n^{(1)} \\ 1 & x_1^{(2)} & \cdots & x_n^{(2)} \\ \vdots & \vdots & \vdots & \vdots \\ 1 & x_1^{(m)} & \cdots & x_n^{(m)} \end{bmatrix} \tag{9}\label{eq:defining-X}$$$

also

$$$Y = \begin{bmatrix} y^{(1)} \\ y^{(2)} \\ \vdots \\ y^{(m)} \end{bmatrix}, \tag{10}\label{eq:defining-Y}$$$

equation \eqref{eq:derivation-5} becomes

\begin{align} J(\theta) &= \frac{1}{2m} (\theta^T X^T - Y^T)(X \theta - Y) &&\text{by transpose property} \tag{11}\label{eq:derivation-6} \\ &= \frac{1}{2m} (\theta^T X^T X \theta - \underbrace{\theta^T X^T Y}_{\text{a scalar}} - \underbrace{Y^T X \theta}_{\text{a scalar}} + Y^T Y) &&\text{by matrix multiplication} \tag{12}\label{eq:derivation-7} \\ &= \frac{1}{2m} (\theta^T X^T X \theta - \theta^T X^T Y - (X \theta)^T Y + Y^T Y) &&\text{by rearranging a scalar} \tag{13}\label{eq:derivation-8} \\ &= \frac{1}{2m} (\theta^T X^T X \theta - \theta^T X^T Y - \theta^T X^T Y + Y^T Y) &&\text{by transpose property} \tag{14}\label{eq:derivation-9} \\ &= \frac{1}{2m} (\underbrace{\theta^T X^T X \theta}_{\text{Part I}} - \underbrace{2 \theta^T X^T Y}_{\text{Part II}} + Y^T Y) &&\text{by summation property} \tag{15}\label{eq:derivation-10} \\ \end{align}

We have arrived into a matrix form from linear regression cost function. Our next step would be:

How can we minimize the cost function in Equation \eqref{eq:derivation-10}?

We will employ the derivation formula from Matrix Calculus; specifically, we use two scalar-by-vector identities with denominator layout (result: column vector). The identities are as follows:

$$$\frac{\partial \mathbf{x}^T \mathbf{A} \mathbf{x} }{\partial \mathbf{x}} = 2 \mathbf{A} \mathbf{x} \tag{16}\label{eq:identity-1}$$$

and

$$$\frac{\partial \mathbf{a}^T \mathbf{x} }{\partial \mathbf{x}} = \frac{\partial \mathbf{x}^T \mathbf{a} }{\partial \mathbf{x}} = \mathbf{a} \tag{17}\label{eq:identity-2}$$$

Now equipped with these identities, let us minimize Equation \eqref{eq:derivation-10} by computing the first derivation of $J(\theta)$; specifically, the Part I is computed with Equation \eqref{eq:identity-1} and Part II with Equation \eqref{eq:identity-2}:

$$$\frac{\partial J }{\partial \theta} = \frac{1}{2m} ( 2 X^T X \theta - 2 X^T Y ) \tag{18}\label{eq:derivation-of-J}$$$

In order to find $\theta$ which minimize Equation \eqref{eq:derivation-10}, we need to solve

\begin{align} \frac{\partial J}{\partial \theta} = 0 &\Longleftrightarrow \frac{1}{2m} ( 2 X^T X \theta - 2 X^T Y ) = 0 \\ &\Longleftrightarrow 2 X^T X \theta - 2 X^T Y = 0 \\ &\Longleftrightarrow 2 X^T X \theta = 2 X^T Y \\ &\Longleftrightarrow X^T X \theta = X^T Y \\ &\Longleftrightarrow \theta = (X^T X)^{-1} X^T Y &&\text{by inverse matrix} \tag{19}\label{eq:final-result} \end{align}

At last, we have derived the normal equation of linear regression model that is

$$$\bbox[5px,border:2px solid blue] {\theta = (X^T X)^{-1} X^T Y}.$$$

Written on August 18, 2019