The workhorse of dimensionality reduction. PCA rotates the data to find the directions of greatest variance — the axes that matter most.
Principal Component AnalysisLive
finding the axes of variation
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PCA finds the directions along which data varies most. The eigenvectors of the covariance matrix are the principal components (cyan = most variance, green = least), and their eigenvalues are the variances along each. Projecting onto the top components is the basis of dimensionality reduction.
Reading this result: PC1 explains 0% of the variance here; the stronger the correlation between features, the more PCA concentrates information on that first component.
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How it works
PCA computes the covariance matrix of the data and takes its eigenvectors as principal components. The first component (cyan) points along the direction of maximum variance; the second (green) is orthogonal to it. The eigenvalues give the variance captured by each, and keeping only the top components compresses data with minimal loss.
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The math, the assumptions, real-world uses, or a code translation — explained for this exact simulation.
Is this PCA principal component analysis tool really free?▾
Yes. Principal Component Analysis runs entirely in your browser using your device's own compute, so local use is free forever. You only pay Compute Tokens if you scale a job to the cloud.
Do I need to install anything?▾
No. Everything runs client-side in a modern browser — no downloads, no license, no account required to start.
Can I save or share my simulation?▾
Create a free account to save projects, and use a shareable embed or minted DOI to publish a live, interactive version anywhere.
How accurate are the results?▾
The solver uses established numerical methods, but results are for research and educational purposes and should be validated against experiment or professional review before you rely on them.