Data Science, Machine Learning, Dimensionality Reduction, Python.
loss. Although, PCA tends to find linear correlations between variables, which is sometimes undesirable. Also, PCA fails in cases where mean and covariance are not enough to define datasets. Further, we may not know how many principal components to keep- in practice, some thumb rules are applied.