PCA Visualizer

Principal Component Analysis � dimensionality reduction and visualization

120 points

Original Data

120 pts

Projected (apply PCA)

PCA Results

Eigenvalues

?1

13.6654

?2

5.9488

Explained Variance

PC169.7%
PC230.3%

Covariance Matrix

[9.6045, 3.8530]

[3.8530, 10.0097]

How it works

Generate: Creates 3 clusters of 2D points with Gaussian noise.

Covariance: Computes the covariance matrix of the data.

PCA: Finds eigenvectors (principal components) and projects data onto them.