One approach for clustering a high dimensional dataset is to use linear transformation, and the most common approaches are PCA and random projection (where random projection arises from the Johnson-Lindenstrauss Lemma). I was wondering why we can't use other random transformation  s like when our transformation matrix $R$ was drawn from a uniform distribution?
Subspace clustering with random transformation
data mining;linear algebra;classification;cluster