# Background

When i.i.d. vector data is collected, then it is generally of interest to represent the data in as low-dimension as possible.

Given $$x_k \in \mathbb{R}^d \sim X$$ for k=1,...,n data points (e.g. if data are images, then d is the number of pixels), one would like to simultaneously i) discover interdependencies within the data and ii) reduce dimensions. It is important to note here that the data is sampled from a distribution and so any downstream statistics (e.g. covariance) are estimates of the true underlying statistic.

Mathematically, this problem can be represented in many ways:

1. Minimizing Cost functions

On the one hand we would like to find a lower-dimensional representation of our data, $$x_k$$. We would like to find the minimization of the following cost function

$$\argmin_P E[ x - Px ]^2$$

where P is a projection operator of rank r. P takes the data points $$x_k$$ and maps them from dimension d to r << d.

2. Maximizing Cost Functions

On the other hand, we can view PCA as a method for maximizing the variance observed in the data, subject to a constraint. If we stack our data samples, $$x_k$$ column-by-column in a data matrix, X, we seek a linear combination of the columns that give us the maximal variance. $$Var(Xa) = a^T Var(X) a$$, where a is a vector of constants to provide linear combinations of X, and $$Var(X)$$ is the covariance matrix of our data.

In addition, we impose a constraint on the vector, a, such that it has unit norm. Thus using the theory of Lagrange multipliers, we can write this as a constrained optimization problem.

$$\argmax_a a^T Var(X) a - \lambda(a^T a - 1)$$

When differentiating with respect to a, and solving the equations, one gets the analytical expression:

$$Var(X) a = \lambda a$$

which is simply an eigenvalue equation, where $$\lambda$$ is an eigenvalue of Var(X) and a is an eigenvector.

# Standard PCA and Algorithm

Standard PCA is commonly implemented in sklearn where the algorithm relies on the Singular Value Decomposition (SVD). Generally, it uses the LAPACK implementation, which supports full, truncated and randomized SVD. Conceptually the algorithm proceeds in 4 steps.

1. Standardization of variable scaling

This step attempts to put all dimensions of the data in the same scale. In the downstream step of the SVD, for variables that have a significantly larger amplitude then other variables, then the variability in those variables will dominate.

Common standardizations include the z-scoring method:

$$x_{k_i} = \frac{x_{k_i} - \mu(x_k)}{\sigma(x_k)}$$

2. Estimate covariance matrix

The next step will compute the covariance matrix of the data. If there are n samples of a d-dimensional data, then the covariance matrix will be $$d \times d$$.

3. Matrix decomposition (SVD/EVD)

Since the covariance matrix is a positive semi-definite (symmetric and non-negative quadratic form), then its singular values are equivalent to its eigenvalues. The singular/eigen vectors of the covariance matrix are ordered by the values of the singular/eigen values.

4. Computing Principle Components

The principle components of the data are obtained by multiplying the data with the singular vector matrix. For example, the first 5 principle components corresponding to the 5 largest singular values can be used to obtain a 5-dimensional representation of the original d-dimensional dataset. We simply take the $$d \times 5$$ truncated matrix and multiply it with our data to obtain n samples now of a 5-dimensional data.

# Statistical Considerations

## Independence

If random variables are independent, then they have 0 correlation. Independence is a stronger assumption then non-correlated. Most of the times, independence is not fully realized; it is just a necessary assumption for mathematical modeling. However, when random variables are not necessarily independent, it is not necessary that they have non-zero correlation. In fact, they may have high correlation.

When performing PCA on correlated data, most likely there will be a few principle components in the data that are captured.

## Centering

When computing the principle components, it is in general common practice to center the columns of the data matrix first.

Geometrically this centers all data points around the origin. PCA attempts at finding an orthogonal rotation to represent the data; note that this rotation occurs about the origin!

If the mean is not subtracted, then essentially the first principle component ends up being the mean vector of the data points, since rotating that mean would result in higher mean-squared error.

## Compressed Sensing (n < d) - "Robust PCA"

When dealing with high-dimension data with high-dimensional noise, PCA may become sensitive to outliers within the dataset. To combat this, we can instead model PCA as a convex optimization problem with the cost function

$$\min_{L,S} ||L||_* + \lambda ||S||_1$$

such that $$X = L + S$$, where L is a low-rank component and S is a sparse component. This minimizes the sum of the singular values for the matrix L and the l1-norm of the matrix S. The sparse matrix is associated with noise.

## Huge Dimensionality (n >> 1, d >> 1)

When you have a large number of samples with extremely high dimensions, the computational workhouse - SVD - becomes computationally expensive. Rather then take a full SVD, it has been shown that taking randomized SVD can capture the principle components of the data.

The randomized SVD approach essentially says that:

• choose a random unitary projection matrix ($$d \times r$$)
• multiply that with your data matrix and apply PCA
• the principle components of that data matrix has high probability of aligning with the principle components of the original large data matrix

Note: The important point here is that the unitary projection matrix must be chosen at random. Good choices are listed in sklearn.

# Questions to Test Understanding

1. Given eigenvalues of the covariance matrix, what is one way to measure variance explained proportion for each component?

Take the eigenvalues and sort them, then for each eigenvalue, divide it by the trace of the covariance matrix. This results in a ratio between the corresponding jth eigenvalue and the total sum of all the eigenvalues, which gives the proportion of variance explained in the principle components.

2. By performing z-score standardization of the data matrix, the covariance matrix of the data is now equivalent to the correlation matrix?

By performing z-score standardization, the columns of the data matrix now all have sample variance equal to 1. Thus, the correlation is defined as:

$$\rho_{X, Y} = \frac{Cov(X, Y)}{\sigma_X \sigma_Y}$$

which is equivalent to the covariance when the sample variances are unitary.

# References:

1. Jolliffe IT, Cadima J. (2016). Principal component analysis: a review and recent developments.Phil. Trans. R. Soc. A 374:20150202. http://dx.doi.org/10.1098/rsta.2015.0202