Back to GalleryPCA
PCA is a linear dimensionality reduction technique. It projects high-dimensional data onto a lower-dimensional subspace by finding orthogonal axes (principal components) that maximize the variance of the projected data.
Key Parameters & Visual Influence:
• Show Eigenvectors: Overlays the principal directions as arrows on the canvas, indicating the primary axes of data variation.
• Animate Projection: Renders a step-by-step transition of points collapsing onto the principal component axes, illustrating the loss of variance in discarded dimensions.
Dataset Studio
CSV format: Headers in first row, last column = label/target. All values must be numeric.
Example: x1, x2, label