The PCA is a change of variables, using the correlations explained by orthogonal directions.

Removing directions with small corresponding correlation is like removing noise. By removing dimension you can compress data, you will also have smoother datas.

By the way, thanks for the site.

The PCA is a change of variables, using the correlations explained by orthogonal directions.

Removing directions with non-representative corresponding correlation is like removing noise. You will only keep significant datas.

By the way, thanks for the site.