---
title: "PCA: an embedding shrink-ray"
url: https://daily.dev/posts/pca-an-embedding-shrink-ray-ayowx3qqb
source_url: http://softwaredoug.com/blog/2026/07/24/pca-shrink-ray.html
type: article
source: "Software Doug"
published: 2026-07-27T12:51:50.010Z
updated: 2026-07-27T12:52:13.605Z
tags: ["machine-learning", "data-science", "vector-search", "embeddings", "numpy"]
reading_time: 6
upvotes: 0
comments: 0
language: en
---

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# PCA: an embedding shrink-ray

**[Software Doug](https://daily.dev/sources/softwaredoug)** · 6 min read · 0 upvotes · 0 comments

## Summary

PCA (Principal Component Analysis) can significantly reduce the memory footprint of vector embeddings. Using MSMarco with MiniLM (384 dimensions) as an example, the post walks through how covariance matrices and eigendecomposition identify redundant dimensions, then shows how to project embeddings into a lower-dimensional space using NumPy. Experimental results show that reducing from 384 to 200 dimensions retains ~88.5% of eigenvalue variance and achieves 0.879 recall vs. brute-force ground truth. The tradeoff depends on the embedding model's efficiency and the corpus distribution — models that already spread information evenly across dimensions compress poorly with PCA.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <http://softwaredoug.com/blog/2026/07/24/pca-shrink-ray.html>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#data-science](https://daily.dev/tags/data-science), [#vector-search](https://daily.dev/tags/vector-search), [#embeddings](https://daily.dev/tags/embeddings), [#numpy](https://daily.dev/tags/numpy)

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