About the project
What it is
This project builds principal component analysis from its linear algebra. I use handwritten digits to see how covariance, eigenvectors, and maximum-variance directions turn a large image space into a smaller representation.
The visualizations show the first two components, the patterns learned by the components, and how well digits can be rebuilt after compression. This makes the tradeoff between fewer dimensions and lost detail visible.
