What are the computational bottlenecks in PCA for large datasets?

Covariance computation and eigen decomposition become expensive in high dimensions.

Standard PCA requires O(n·d² + d³) complexity due to covariance matrix computation and eigen decomposition. For large datasets, this becomes infeasible, requiring alternatives like randomized PCA or incremental PCA that approximate principal components efficiently.