What is dimensionality reduction?

Dimensionality reduction takes data with hundreds or thousands of dimensions, like model embeddings, and compresses it into just two or three while keeping the important relationships intact. Points that were close in the original space stay close in the reduced space, which lets you plot an entire dataset and see its structure at a glance.

It is the standard way to turn abstract embeddings into an interactive map you can actually explore.

Key takeaways

It compresses high-dimensional data into a few dimensions.
It preserves structure so nearby points stay nearby.
It makes embeddings visualizable for exploration and clustering.

Common methods

Widely used dimensionality reduction techniques.

Widely used dimensionality reduction techniques.
Method	Best for
PCA	Fast linear reduction and denoising
t-SNE	Detailed local structure in visualizations
UMAP	Balancing local and global structure at scale

How it works

Linear methods like PCA find the directions of greatest variance and project onto them. Nonlinear methods like t-SNE and UMAP optimize a low-dimensional layout so that neighborhoods in the original space are preserved, which is what produces the familiar clustered scatter plots of embeddings. The reduced coordinates are then plotted, often colored by label or metadata.

Why it matters

Embeddings are powerful but impossible to inspect directly, and dimensionality reduction is what makes them human-readable. Paired with clustering, it turns a dataset into a navigable map, which is the foundation of visual, embedding-driven data curation.

Frequently asked questions

Why not just look at the raw embeddings?

Embeddings have too many dimensions to visualize or reason about directly, so they must be projected down to two or three to be seen.

Which method should I use?

PCA is fast and linear, t-SNE emphasizes local detail, and UMAP balances local and global structure well at scale. The choice depends on dataset size and what you want to see.

Jesse Mostipak is the SEO and Content Manager at Voxel51, where the work is helping humans find and trust what the brand knows, and teaching the Google knowledge graph and the LLMs answering on their behalf to do the same. That question, how knowledge gets built inside a system, is one Jesse has been chasing for years. Earlier versions of it ran through a New York City high school science classroom, data science and machine learning, and developer relations at Kaggle, Posit (formerly RStudio), and Baseten. The answer doesn't change much depending on whether the learner is a teenager, a software engineer, or a knowledge graph. Jesse holds a Master's in Education from CUNY Hunter College. LinkedIn

Dimensionality reduction

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