# dimensionality-reduction

> Browse 5 items tagged with dimensionality-reduction.

## Items

- [GleanVec: Accelerating vector search with minimalist nonlinear dimensionality reduction](https://vectordb.ever.works/items/gleanvec-accelerating-vector-search-with-minimalist-nonlinear-dimensionality-reduction) — Paper by Tepper et al. proposing GleanVec, a method to accelerate vector search using minimalist nonlinear dimensionality reduction. Improves efficiency for high-dimensional vector queries.
- [LeanVec: Search Your Vectors Faster by Making Them Fit](https://vectordb.ever.works/items/leanvec-search-your-vectors-faster-by-making-them-fit) — Research paper introducing LeanVec, a technique to accelerate vector search by reducing vector dimensionality while preserving search accuracy. Published as an arXiv preprint in 2023 by Mariano Tepper et al.
- [PaCMAP](https://vectordb.ever.works/items/pacmap-dimensionality-reduction) — Pairwise Controlled Manifold Approximation - a dimensionality reduction technique that preserves both local and global structure better than UMAP or t-SNE. Particularly effective for visualizing complex embedding spaces.
- [UMAP](https://vectordb.ever.works/items/umap-dimensionality-reduction) — Uniform Manifold Approximation and Projection - a dimensionality reduction technique used for visualizing high-dimensional vector embeddings and compressing vectors while preserving structure. Popular for embedding analysis and visualization.
- [UMAP](https://vectordb.ever.works/items/umap) — Uniform Manifold Approximation and Projection - a non-linear dimensionality reduction technique that preserves both local and global data structure. More scalable than t-SNE while maintaining superior visualization quality and cluster separation for high-dimensional embeddings.

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