When dimension reducing data in RD with PCA, the choice of embedding dimension is crucial. Many

heuristics exist to estimate a good dimension. One is to choose the embedding dimension d$$ to be the smallest

dimension such that some proportion $($say$,\mathrm{.}95)$ of the variance of the data is preserved by projecting onto

the first d$$ principal components