Dimensionality Reduction
Frequently Asked Questions
What is Principal Component Analysis (PCA)?▼
PCA is a linear dimensionality reduction method that projects data onto directions of maximum variance (principal components).
Why reduce dimensions for vector search?▼
To lower computational latency and storage costs in vector databases by matching smaller embedding lengths.
Quick Facts
- CategoryMathematical Foundations
- Key ApplicationEmbedding visualization (t-SNE, UMAP), data compression, and clustering acceleration
Coverage Trend12 Weeks
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