Topic Segmentation - should it be done in Raw, TfIdf or Semantic Space?
Clustering method can applied directly to TfIdf matrix (which will be generally sparse) or to documents in the derived semantic space like LSI.  Since LSI embedding is a dimensionality/noise reduction step, it is a good idea to cluster on documents embedded in semantic space. Clustering methods will generally use Euclidian distance and will probably perform better on dense matrix. If the clustering algorithm implementation that you use doesn't support sparse data out of the box (most implementations don't) then definitely use a transformation like LSI first and then apply clustering.
LSI method itself is a matrix decomposition operation on the original TfIdf matrix. So applying matrix decomposition on top of LSI is not needed.