How does PCA help mitigate the curse of dimensionality?
Updated May 15, 2026
Short answer
PCA reduces dimensions while preserving variance.
Deep explanation
Principal Component Analysis projects data onto orthogonal axes capturing maximum variance, reducing noise and redundancy.
Real-world example
Image compression in computer vision pipelines.
Common mistakes
- Assuming PCA preserves all information.
Follow-up questions
- What is explained variance?
- When does PCA fail?