Understanding Dimensionality Reduction
Why Reduce Dimensions?
High‑dimensional data can be noisy, hard to visualize, and computationally expensive. Dimensionality reduction transforms data into a lower‑dimensional space while preserving its core structure, making analysis more tractable.
Popular Techniques
- Principal Component Analysis (PCA) – Linear method that projects data onto orthogonal axes of maximum variance.
- t‑Distributed Stochastic Neighbor Embedding (t‑SNE) – Non‑linear technique focusing on preserving local neighborhoods.
- Uniform Manifold Approximation and Projection (UMAP) – Fast, scalable, preserves both local and global structure.
Interactive Demo
Explore how PCA compresses a synthetic 5‑dimensional dataset into 2 dimensions.
When to Use Dimensionality Reduction
Use it when you need to:
- Visualize high‑dimensional data.
- Speed up downstream algorithms.
- Reduce noise and improve model generalization.