each eigenvectors-->each eigenvector

src/shogun/converter/Isomap.h

@@ -40,7 +40,7 @@ class CDistance;
  * - Compute squared distances matrix \f$D\f$ such as \f$D\{i,j\} = d^2(x_i,x_j)\f$.
  * - Relax distances with shortest(so-called geodesic) distances on the sparse neighbourhood graph (e.g. with sparse Dijkstra algorithm).
  * - Center the matrix \f$D\f$ with subtracting row mean, column mean and adding to the grand mean. Multiply \f$D\f$ element-wise with \f$-0.5\f$.
- * - Compute embedding with the \f$t\f$ eigenvectors that correspond to the largest eigenvalues of the matrix \f$D\f$; normalize these vectors dividing each eigenvectors with square root of its corresponding eigenvalue. Form the final embedding with eigenvectors as rows and projected feature vectors as columns.
+ * - Compute embedding with the \f$t\f$ eigenvectors that correspond to the largest eigenvalues of the matrix \f$D\f$; normalize these vectors dividing each eigenvector with square root of its corresponding eigenvalue. Form the final embedding with eigenvectors as rows and projected feature vectors as columns.
 
  * It is possible to apply preprocessor to specified distance using
  * apply_to_distance.