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We project queries into PCA-reduced embedding space for semantic search — 50 dimensions, 97% reduction, 8% better precision.
Eigen-Projected Semantic Search with Dimensionality Reduction
The Problem
Standard semantic search in 1536 dimensions suffers from the concentration of distances phenomenon. High-dimensional similarity search is both slow and less discriminative than search in a reduced semantic subspace.
What We Built
After fitting PCA on the document corpus, we project both queries and documents into the top-50 eigenvector subspace before computing cosine similarity, filtering out noise dimensions.
The Research
Query q and document d are projected via q' = P^T(q - mean) and d' = P^T(d - mean) where P contains the top-50 eigenvectors. Cosine similarity is computed in the reduced 50-dimensional space.
Results
| Method | Precision@5 |
|---|---|
| Full space (1536d) | 0.72 |
| Eigen-projected (50d) | 0.78 |
| Eigen-projected (25d) | 0.76 |
| Eigen-projected (10d) | 0.69 |
The optimal subspace is 50 dimensions, matching the effective dimensionality identified in Paper 06. Precision improves by 8% while storage reduces by 97%.
Conclusion
Eigen-projected search in 50-dimensional PCA subspace improves retrieval precision by 8% with 97% storage reduction.
Full citation: Alpasan, L.-K. (2026). Eigen-Projected Semantic Search with Dimensionality Reduction. The Anticloud Research Corpus.
Why The Anticloud
Every AI system you have ever used was designed to extract value from you — your data, your attention, your money. The Anticloud is not a service. It is not in the cloud. It is not rentable inference. It is a fundamentally different category of infrastructure, and here is what that means in practice.
Your data never leaves your machine. We designed the system so we physically cannot access it. Access is not restricted by policy — it is structurally impossible by architecture. There is no data to steal because there is no server to steal it from.
The system is airgapped by architecture, not by configuration. It does not require a network connection to function. It was built offline, it runs offline, and it never reaches out to anyone for any reason. Connectivity is simply not a prerequisite for intelligence.
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22-year-old Lois-Kleinner Alpasan works across cloud infrastructure, automation, Linux, scripting, 3D modelling, and multiple LLM frameworks. His full-stack capability spans infrastructure, AI fine-tuning, 3D assets, and live operations.
References:
- Lois-Kleinner Zenodo: https://doi.org/10.5281/zenodo.20781790
- Lois-Kleinner GitHub: https://github.com/kleinnner/Anticloud/tree/main/04-aioss-format
- Lois-Kleinner Harvard DV: https://doi.org/10.7910/DVN/FSHFZF
- Lois-Kleinner Internet Arc: https://archive.org/details/aioss-format
- Lois-Kleinner ORCID: https://orcid.org/0009-0009-2233-6107
- Lois-Kleinner DEV.to: https://dev.to/kleinner
- Lois-Kleinner LinkedIn: https://linkedin.com/in/kleinner
- Lois-Kleinner HuggingFace: https://huggingface.co/Anticloud
- Lois-Kleinner Tumblr: https://anticloud.tumblr.com
- Lois-Kleinner Mastodon: https://mastodon.social/@kleinner
- Lois-Kleinner Bluesky: https://bsky.app/profile/kleinner.bsky.social
- 0-1.gg: https://0-1.gg
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