# Principal Component Analysis Background

## 0. Problem setup

Suppose we observe \(n\) data points \[ X_1, X_2, \ldots, X_n \in \mathbb{R}^p. \]

We write the data matrix as \[ X = \begin{bmatrix} X_1^\top \\ X_2^\top \\ \vdots \\ X_n^\top \end{bmatrix} \in \mathbb{R}^{n \times p}, \] where each row corresponds to one observation and each column corresponds to one variable.

Principal Component Analysis (PCA) is a dimension reduction method that represents high-dimensional data through a small number of orthogonal directions that preserve as much variation as possible.

To do this, PCA finds a direction \(v \in \mathbb{R}^p\) such that the projected values

\[ X_i^\top v, \qquad i = 1,\ldots,n, \]

are as spread out as possible. A direction with larger projected variance captures more variation in the data.

## 1. Population PCA

Let \(Y \in \mathbb{R}^p\) be a random vector with covariance matrix \(\Sigma = \operatorname{Cov}(Y)\).

### First principal component direction

The first population principal component direction is defined as

\[ v_1 = \arg\max_{\|v\|_2 = 1} \operatorname{Var}(v^\top Y) = \arg\max_{\|v\|_2 = 1} v^\top \Sigma v. \]

Thus, \(v_1\) is the unit direction that maximizes the variance of the projection of \(Y\) onto \(v\).

### Subsequent principal component directions

Similarly, each subsequent principal component directions are obtained by maximizing the variance of the projection of \(Y\) onto that direction, while being orthogonal to the previously chosen directions.

For \(k \geq 2\),

\[ v_k = \arg\max_{\|v\|_2 = 1} v^\top \Sigma v \quad \text{subject to} \quad v^\top v_j = 0, \qquad j = 1,\ldots,k-1. \]

Therefore, PCA gives a sequence of mutually orthogonal directions \(v_1, v_2, \ldots, v_p\) ordered by decreasing projected variance.

## 2. Eigenvalue decomposition

The solutions to the above variance maximization problems are obtained from the eigenvalue decomposition of the covariance matrix \(\Sigma\).

We can write

\[ \Sigma = V \Lambda V^\top \] where \[ V = [v_1,\ldots,v_p], \quad \Lambda = \operatorname{diag}(\lambda_1,\ldots,\lambda_p), \quad \text{and} \quad \lambda_1 \geq \lambda_2 \geq \cdots \geq \lambda_p \geq 0. \]

The eigenvectors \(v_1,\ldots,v_p\) are the population principal component directions, and each eigenvalue \(\lambda_j\) gives the variance of the projection of \(Y\) onto \(v_j\).

### Lagrangian formulation

To see why the PCA directions are eigenvectors, consider the first principal component problem

\[ \max_{\|v\|_2 = 1} v^\top \Sigma v. \]

Using the constraint \(v^\top v = 1\), define the Lagrangian

\[ \mathcal{L}(v,\lambda) = v^\top \Sigma v - \lambda (v^\top v - 1). \]

Taking the derivative with respect to \(v\) and setting it equal to zero gives

\[ \frac{\partial \mathcal{L}}{\partial v} = 2\Sigma v - 2\lambda v = 0. \]

Therefore,

\[ \Sigma v = \lambda v. \]

Hence, the optimizer \(v\) must be an eigenvector of \(\Sigma\), and the corresponding Lagrange multiplier \(\lambda\) is the associated eigenvalue.

For a unit eigenvector \(v\), we have

\[ v^\top \Sigma v = v^\top (\lambda v) = \lambda v^\top v = \lambda. \]

Thus,

\[ \max_{\|v\|_2 = 1} \operatorname{Var}(v^\top Y) = \max_{\|v\|_2 = 1} v^\top \Sigma v = \lambda_{\max}. \]

The first principal component direction is the eigenvector corresponding to the largest eigenvalue \(\lambda_1\), and the maximum projected variance is \(\lambda_1\).

Repeating this procedure under orthogonality constraints gives the remaining eigenvectors. Therefore,

\[ \Sigma v_j = \lambda_j v_j, \qquad j = 1,\ldots,p, \] with

\[ \lambda_1 \geq \lambda_2 \geq \cdots \geq \lambda_p \geq 0. \]

The \(\ell\)-th eigenvalue can be interpreted as the variance of the projected random variable along the \(\ell\)-th principal component direction

\[ \lambda_\ell = v_\ell^\top \Sigma v_\ell = \operatorname{Var}(v_\ell^\top Y). \]

## 3. Sample PCA

In practice, the population covariance matrix \(\Sigma\) is unknown, so we use the sample covariance matrix \(\hat\Sigma\) instead.

Let \[ \bar X = \frac{1}{n} \sum_{i=1}^n X_i \]

be the sample mean. The sample covariance matrix is

\[ \hat\Sigma = \frac{1}{n-1} \sum_{i=1}^n (X_i-\bar X)(X_i-\bar X)^\top. \]

Equivalently, if \(X_c\) denotes the centered data matrix, then

\[ \hat\Sigma = \frac{1}{n-1} X_c^\top X_c. \]

### Sample principal component directions

The first sample principal component direction is

\[ \hat v_1 = \arg\max_{\|v\|_2 = 1} v^\top \hat\Sigma v. \]

For \(\ell \geq 2\),

\[ \hat v_\ell = \arg\max_{\|v\|_2 = 1} v^\top \hat\Sigma v \quad \text{subject to} \quad v^\top \hat v_j = 0, \qquad j = 1,\ldots,\ell-1. \]

The \(\ell\)-th sample principal component direction \(\hat v_\ell\) is obtained as the \(\ell\)-th eigenvector of \(\hat\Sigma\), and the corresponding sample eigenvalue is

\[ \hat\lambda_\ell = \hat v_\ell^\top \hat\Sigma \hat v_\ell. \]

This value is the sample variance of the data projected onto the direction \(\hat v_\ell\).

From an estimation point of view, \(\hat v_\ell\) and \(\hat\lambda_\ell\) estimate the population quantities \(v_\ell\) and \(\lambda_\ell\), respectively.

## 4. Principal component score and scree

### PC scores

Assume that the data matrix \(X \in \mathbb{R}^{n \times p}\) has been centered. Let \(\hat v_\ell\) be the \(\ell\)-th sample principal component direction. The \(\ell\)-th PC score vector is defined as

\[ z_\ell = X \hat v_\ell \in \mathbb{R}^n. \]

Equivalently, the \(i\)-th entry of \(z_\ell\) is

\[ z_{i\ell} = x_i^\top \hat v_\ell, \]

which is the coordinate of the \(i\)-th observation after projection onto the \(\ell\)-th principal component direction.

If the first \(k\) principal component directions are used, the score matrix is

\[ Z_k = X \hat V_k, \qquad \hat V_k = [\hat v_1,\ldots,\hat v_k]. \]

A score plot usually displays two score vectors, such as \((z_1,z_2)\), as a two-dimensional scatter plot. It is used to explore low-dimensional patterns in the data, such as clusters, outliers, or separation between groups.

### PC Scree values

Let \[ \hat\lambda_1 \ge \hat\lambda_2 \ge \cdots \ge \hat\lambda_p \ge 0 \]

be the eigenvalues of the sample covariance matrix \(\hat\Sigma\). We call \(\hat\lambda_\ell\) the \(\ell\)-th sample scree value. It represents the sample variance explained by the \(\ell\)-th principal component direction.

In terms of the score vector \(z_\ell = X\hat v_\ell\),

\[ \hat\lambda_\ell = \operatorname{Var}(X\hat v_\ell) = \hat v_\ell^\top \hat\Sigma \hat v_\ell = \frac{1}{n-1}\|z_\ell\|_2^2. \]

A scree plot displays the sequence of eigenvalues

\[ \hat\lambda_1,\hat\lambda_2,\ldots,\hat\lambda_p, \]

or the proportion of variance explained by each principal component,

\[ \widehat{\mathrm{PVE}}_\ell = \frac{\hat\lambda_\ell}{\sum_{j=1}^p \hat\lambda_j}. \]

It summarizes how much variation is explained by each principal component. Since the eigenvalues are ordered decreasingly, the scree plot is often used to decide how many principal components should be retained.

### Relationship between score plots and scree plots

The scree plot and the score plot show PCA results in different ways.

The score plot shows the observations after projection onto selected principal component directions. For the \(\ell\)-th principal component direction \(\hat v_\ell\), the scree value is

\[ \hat\lambda_\ell = \hat v_\ell^\top \hat\Sigma \hat v_\ell = \frac{1}{n-1}\|X\hat v_\ell\|_2^2. \]

This is the sample variance of the projected scores \(X\hat v_\ell\).

The score plot shows the observations in the principal component coordinate system. If

\[ \hat V_k = [\hat v_1,\ldots,\hat v_k], \]

then the score matrix is \[ Z_k = X\hat V_k. \]

The rows of \(Z_k\) are the low-dimensional coordinates of the observations.

Therefore, the scree plot helps decide which components are important, while the score plot visualizes the data using those components. For instance, when the first two scree values are large, the two-dimensional score plot

\[ (X\hat v_1,\; X\hat v_2) \]

can give an informative view of the main structure in the data.

## References

Jolliffe, I. T. and Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202.