**5.3 Randomized PCA**

The PCs are estimated using the low-rank matrix approximation in traditional PCA. However, this strategy becomes costly with large datasets and makes the entire process challenging to scale. One can approximate the first K PCs faster than traditional PCA by randomizing how the dataset singular value decomposition occurs.
