Uses For PCA Other Than Dimensionality Reduction Part I¶

Decorrelation, Factor Discovery, and Noise Modeling¶

Principal Component Analysis (PCA) is frequently applied in machine learning as a sort of black box dimensionality reduction technique. However with a deeper understanding of what PCA is and what it does we can use it for all manner of other tasks e.g.

Decorrelating Variables
Semantic Factor Discovery
Empirical Noise Modeling
Missing Data Imputation
Example Generation
Anomaly Detection
Patchwise Modeling
Noise Reduction

We will demonstrate how to use PCA for these purposes on an example face dataset. In this first post we will handle up till empirical noise modeling and handle the rest in subsequent parts.

Asymptotic Labs (Posts about Noise Modeling)

Uses for PCA other than dimensionality reduction part 1

Uses For PCA Other Than Dimensionality Reduction Part I¶

Decorrelation, Factor Discovery, and Noise Modeling¶