DOC better docstring for TruncatedSVD (scikit-learn#7651)

Author: ogrisel

sklearn/decomposition/truncated_svd.py

@@ -26,9 +26,10 @@ class TruncatedSVD(BaseEstimator, TransformerMixin):
     """Dimensionality reduction using truncated SVD (aka LSA).
 
     This transformer performs linear dimensionality reduction by means of
-    truncated singular value decomposition (SVD). It is very similar to PCA,
-    but operates on sample vectors directly, instead of on a covariance matrix.
-    This means it can work with scipy.sparse matrices efficiently.
+    truncated singular value decomposition (SVD). Contrary to PCA, this
+    estimator does not center the data before computing the singular value
+    decomposition. This means it can work with scipy.sparse matrices
+    efficiently.
 
     In particular, truncated SVD works on term count/tf-idf matrices as
     returned by the vectorizers in sklearn.feature_extraction.text. In that