PCA (Principal Component Analysis)

Principal component analysis (PCA) is a dimensionality reduction technique that turns data with many features into a smaller set of new features, called principal components, while keeping as much of the original variation as possible. In SEO work it is most often used to squeeze high-dimensional text vectors down to two dimensions so clusters can be plotted and inspected.

How it works

PCA looks for the directions in the data along which points vary the most:

• the first principal component is the direction with the greatest variance,
• the second is the direction with the next greatest variance, at right angles to the first, and so on,
• each data point is then projected onto the first few components.

Keeping two components gives you x and y coordinates for a scatter plot. The “explained variance ratio” tells you how much of the original spread those components retain. PCA is linear: it finds straight-line directions, which makes it fast, stable and repeatable, but less able to show curved or tangled structure than non-linear methods such as UMAP or t-SNE.

How it’s used in SEO and marketing

• Visualising clusters of keywords, entities or pages after K-means.
• Reducing noise before clustering numeric data such as competitor metrics or customer behaviour.
• Exploring embeddings from sentence-transformer models to see whether topics separate.
• Finding the main drivers in a dataset with many correlated metrics, such as several engagement measures that move together.

Example

In MLforSEO’s keyword-to-topic mapping guide, entities are turned into TF-IDF vectors with one dimension per term, grouped with K-means, then reduced with PCA to two dimensions. The result is a scatter plot where each point is an entity, coloured by cluster, so you can see which clusters are distinct and which overlap.

Limitations and things to watch

• A 2D plot of very high-dimensional data keeps only part of the information; check the explained variance before reading too much into distances.
• Components are combinations of the original features and are hard to name.
• Scale numeric features first, or features with large values dominate.
• PCA centres the data first, which is costly on large sparse TF-IDF matrices. scikit-learn’s TruncatedSVD (the method behind LSA) is the usual alternative there.
• For visualising embeddings, UMAP often separates clusters more clearly; use PCA when you want a fast, deterministic view.

Learn it on MLforSEO

• Different ways to Map Keywords to topics – with Supervised and Unsupervised ML approaches
• What Machine Learning Clustering Is, and Where It Fits in SEO
• Map Keywords to Topics: Supervised (sBERT + Fuzzy) vs. Unsupervised (BERTopic) (Notebook)
• The Semantic AI-powered SEO Keyword Research course, which uses PCA to visualise entity clusters