DOC Fix a typo in comment
@@ -236,7 +236,7 @@ class KernelPCA(TransformerMixin, BaseEstimator):
# if v is an eigenvector of K
# then Phi(X)v is an eigenvector of Phi(X)Phi(X)'
# if u is an eigenvector of Phi(X)Phi(X)'
- # then Phi(X)'u is an eigenvector of Phi(X)Phi(X)'
+ # then Phi(X)'u is an eigenvector of Phi(X)'Phi(X)
#
# At this stage our self.alphas_ (the v) have norm 1, we need to scale
# them so that eigenvectors in kernel feature space (the u) have norm=1
