Usually LSI/LSA is done on a TermFrequency matrix (each row a document, each column a term) to reduce the dimensionality along the terms dimension (i.e. there are too many words). In that way we would expect that similar terms are going to be put together and we obtain a cleaner / more readable representation of the documents.
My questions is: what if we apply the same technique on the document axis? Was this already studied? Does is makes sense at all?
The obtained matrix will have the same amount of columns but fewer rows. I would expect each row to represent a cluster of similar documents, but there are two things that are not too clear:
what is the ordering telling us? The first row corresponds to the highest eigenvalue, but I am not sure how to interpret that.
is there any interpretation of the coefficients in each row? Are those the terms that appear more frequently / have higher weight in the "cluster"?