PCA Wave Interference

2025

PCA Wave Interference: Iris Dataset

Spectral signatures from principal component decomposition

X-Axis Observation

PC scores: [-2.26, 0.48, 0.13]

Y-Axis Observation

PC scores: [1.41, 0.26, -0.06]

X-Axis Waves

c1 (base frequency)1.00

Y-Axis Waves

c2 (base frequency)1.00

2D Interference Pattern

Cutoff Threshold0.50

Y Wave Phase Offset0.00π

Decomposes Iris dataset observations into their three principal component waves, showing how each component contributes to the final superposition. Adjust frequency and phase parameters to explore the resulting 2D interference patterns.

Appears in themes

Machine Learning

Shows the decomposition explicitly: PC1 contributes most variance (largest amplitude), PC2 and PC3 add detail. The superposition is a weighted sum where eigenvalues determine each component's contribution.

Statistics

The frequency parameters (c1, c2) act as zoom levels into the wave structure. Phase offset reveals how temporal alignment between X and Y waves creates or destroys interference nodes.

Introspection

What does it mean to "see" a statistical transform? This artifact makes PCA tangible - the abstract rotation of coordinate axes becomes visible wave interference.