STYLE

Examples/DimensionReduction/ICAExample.cxx

@@ -38,7 +38,7 @@
 // orthogonal linear combinations, but the criterion of Fast ICA is
 // different: instead of maximizing variance, it tries to maximize
 // stastistical independance between components.
-// 
+//
 // In the Fast ICA algorithm \cite{hyvarinen1999fast},
 // statistical independance is mesured by evaluating non-Gaussianity
 // of the components, and the maximization is done in an iterative way.

Examples/DimensionReduction/NAPCAExample.cxx

@@ -35,8 +35,8 @@
 // efficient method based on the inner product in order to compute the
 // covariance matrix.
 //
-// The Noise-Adjusted Principal Component Analysis transform is a sequence 
-// of two Principal Component Analysis transforms. The first transform is based 
+// The Noise-Adjusted Principal Component Analysis transform is a sequence
+// of two Principal Component Analysis transforms. The first transform is based
 // on an estimated covariance matrix of the noise, and intends to whiten the
 // input image (noise with unit variance and no correlation between
 // bands).