Karl Pearson is credited with the development of PCA in 1901, but it gained popularity with the increased availability of computers, which allowed for multivariate statistical computations¹ at scale. PCA is very effective for visualizing and exploring high-dimensional datasets, or data with many features, as it can easily identify trends, patterns, or outliers.

PCA is commonly used for data preprocessing for use with machine learning algorithms. It can extract the most informative features from large datasets while preserving the most relevant information from the initial dataset. This reduces model complexity as the addition of each new feature negatively impacts model performance, which is also commonly referred to as the “curse of dimensionality.” By projecting a high-dimensional dataset into a smaller feature space, PCA also minimizes, or altogether eliminates, common issues such as multicollinearity and overfitting. Multicollinearity occurs when two or more independent variables are highly correlated with one another, which can be problematic for causal modeling. Overfit models will generalize poorly to new data, diminishing their value altogether. PCA is a commonly used approach within regression analysis but it is also leveraged for a variety of use cases, such as pattern recognition, signal processing, image processing, and more.

While there are other variations of PCA, such as principal component regression and kernel PCA, the scope of this article will focus on the primary method within current literature.

PCA is a dimension reduction technique like linear discriminant analysis (LDA). In contrast to LDA, PCA is not limited to supervised learning tasks. For unsupervised learning tasks, this means PCA can reduce dimensions without having to consider class labels or categories. PCA is also closely related to factor analysis. They both reduce the number of dimensions or variables in a dataset while minimizing information loss. PCA breaks down variables into a subset of linearly independent principal components. Factor analysis, however, is generally used to understand underlying data structures, focusing on latent variables, or unmeasured factors, that capture a variable’s spread.

PCA and k-means clustering are both unsupervised machine learning techniques used for data analysis, but they have different goals and methods. PCA is used to reduce the dimensionality of the data, while k-means clustering groups data points together based on similarity. The technique you select depends on the specific dataset and goals of your analysis.

PCA creates new variables, such as principal components, that are linear combinations of the original variables. PCA takes a dataset with multiple variables as input, and it produces a dataset into a lower subspace, that is, a reduced dataset with fewer variables. It is often used in exploratory data analysis for building predictive models, but it is also used in data preprocessing for dimensionality reduction.

K-means is a clustering algorithm that assigns data points to clusters based on their distance from the cluster centers. It takes a dataset with one or more variables as input, and it produces a set of clusters with similar data points. It is often used to cluster data for a variety of use cases, such as image segmentation, customer segmentation, and anomaly detection.

PCA summarizes the information content of large datasets into a smaller set of uncorrelated variables known as principal components. These principal components are linear combinations of the original variables that have the maximum variance compared to other linear combinations. These components capture as much information from the original dataset as possible.

This statistical technique involves both linear algebra and matrix operations, and it transforms the original dataset into a new coordinate system that is structured by the principal components. The eigenvectors and eigenvalues from the covariance matrix that underpin the principal components allow for the analysis of these linear transformations.

Imagine you have mapped out a dataset with multiple features, resulting in a multi-dimensional scatterplot. Eigenvectors provide the direction of variance in the scatterplot. Eigenvalues are the coefficients of the eigenvectors; these denote the importance of this directional data. Therefore, a high eigenvalue means that the corresponding eigenvector is more critical. Since principal components represent the directions of maximum variance in the data, they are also the eigenvectors of the covariance matrix.

Two major components are calculated in PCA: the first principal component (PC1) and the second principal component (PC2).

The first principal component (PC1) is the direction in space along which the data points have the highest or most variance. It is the line that best represents the shape of the projected points. The larger the variability captured in the first component, the larger the information retained from the original dataset. No other principal component can have a higher variability.

We calculate the second principal component (PC2) in the same way as PC1. PC2 accounts for the next highest variance in the dataset and must be uncorrelated with PC1. That is, PC2 must be orthogonal, that is perpendicular, to PC1. This relationship can also be expressed as the correlation between PC1 and PC2 equals zero. 

A scatterplot is typically used to show the relationship between PC1 and PC2 when PCA is applied to a dataset. PC1 and PC2 axis will be perpendicular to each other.

If there are any subsequent components, then they would also retain the same properties, where they would not be correlated with other components and explain any remaining variation.