Given a data matrix X n$$d$,$ where d is much smaller than n and k$=$rank$($X$),$ if we project our data onto a k$-$dimensional subspace using PCA, our projection will have zero reconstruction error $($in other words, we find a perfect representation of our data, with no information loss$).$