> ML_ALGORITHM // PRINCIPAL-COMPONENT-ANALYSIS_v1.0
Principal Component Analysis (PCA & TruncatedSVD)
Orthogonal linear transformation that projects high-dimensional data onto orthogonal axes of maximum variance.
Linear Dimensionality Reductionclassical-unsupervisedhigh-intrinsicsmall (<1k)
Back to All AlgorithmsComputational Complexity
Training Complexity:O(min(n^3, p^3) + n * p * min(n, p))
Inference Complexity:O(p * components)
Hardware Profile
CPU Friendly:Yes
Requires GPU:No
Memory Footprint:low
Interpretability & Data
Interpretability Tier:high-intrinsic
Training Data Needs:small (<1k)
Interpretability Assessment
Principal axes are linear combinations of features whose loadings indicate exact contribution.
Suitable Tasks & Supported Modalities
Suitable Tasks:
dimensionality reductionfeature extractionanomaly detection
Supported Modalities:
tabularimage
Implementing Libraries
Foundational Literature
Common Pitfalls & Warnings
- Applying PCA without prior zero-centering and unit-variance feature normalization