The particular technique used in LSI is singular-value decomposition (SVD for short), in which a large term-by-document matrix is decomposed into a set of orthogonal factors from which the original matrix can be approximated by linear combination.
sVD on the large term-by-document matrix whose size increases with the size of the training corpus brings huge computation costs.