Clustering and latent structure · MLD-0940
Principal component analysis
A linear projection onto orthogonal directions capturing maximum variance successively.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Compress correlated numerical features.
Original caveat
High variance does not necessarily mean task relevance.
Context and aliases
Level: Intermediate. Stages: Training · Data · Evaluation.
Tags: ML · DL.
Aliases: PCA.
Related concepts
Concepts linking here
Original references
- scikit-learn: clustering ↗ · Reference link
- scikit-learn: matrix decomposition ↗ · Reference link
- scikit-learn: manifold learning ↗ · Reference link
- scikit-learn: novelty and outlier detection ↗ · Reference link
- Original reference URL ↗