PCA Projection Explorer

Representation Learning

PCA Projection Explorer

Variance, projection, reconstruction, and information loss.

What This Shows

Manipulate a two-dimensional data cloud and observe how principal components rotate, project, and reconstruct the data. The applet gives a concrete view of what dimensionality reduction preserves, what it discards, and why that matters before feeding compressed representations into downstream models.

PCA Linear algebra Dimensionality reduction Feature compression Reconstruction

Technical Challenge

Reducing dimensionality without losing the structure needed for the task.

My Contribution

Designed a visual explanation that connects covariance geometry to model-ready representations.

Industry Transfer

Embeddings, denoising, exploratory analysis, feature engineering, scientific data reduction.

Connected Work

VVV/VVVX feature extraction and DEEPz high-dimensional regression.