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Principal Component Analysis
beginnerunsupervised
parametricdimensionality_reductionrepresentationtabular
PCA rotates data onto new perpendicular axes ordered by how much variance they capture.
Mechanisms
linear projectionvariance maximizationmatrix factorization
Properties
interpretablerepresentation learning
Constraints
requires scalingsensitive to outliers
Practical profile
- Explainability
- medium
- Training cost
- low
- Inference cost
- low
- Data appetite
- low