Study
Machine Learning · PCA
Principal component analysis: rotating data onto directions of maximum variance, explained variance ratio, and compressing the digits dataset.
- PCA finds orthogonal directions that capture as much variance as possible, in order.
- Keeping the components that explain about 90–95% of the variance often removes most of the dimensions.