# Dimensionality Reduction Practice

> ML preparation set.

- Stable ID: `dimensionality-reduction-practice`
- Area: machine-learning
- Kind: practice
- Timebox: 25 minutes

Relate PCA variance objectives to covariance eigenvectors and SVD.

## Instructions

1. Center the data conceptually before applying PCA.

## Ordered items

1. [PCA direction and explained variance](https://mlprep.iwase.dev/machine-learning/dimensionality-reduction/original-ml-pca/) — `original-ml-pca` (7 min)
2. [Low-rank error and explained variance](https://mlprep.iwase.dev/mathematics/linear-algebra/original-la-low-rank/) — `original-la-low-rank` (8 min)

## Completion

Explain both reconstruction-error and retained-variance views of PCA.