How many principal components to take?

Question

I know that principal component analysis does a SVD on a matrix and then generates an eigen value matrix. To select the principal components we have to take only the first f

Answer 1

There are a number of heuristics use for that.

E.g. taking the first k eigenvectors that capture at least 85% of the total variance.

However, for high dimensionality, these heuristics usually are not very good.