diff --git a/Examples/DimensionReduction/ICAExample.cxx b/Examples/DimensionReduction/ICAExample.cxx
index c8b77c13100d189f2f56e02959fc131ae5a0fce1..fccd534921b7749a735072ec55bf98aaa0b8f2db 100644
--- a/Examples/DimensionReduction/ICAExample.cxx
+++ b/Examples/DimensionReduction/ICAExample.cxx
@@ -31,16 +31,16 @@
//
// This example illustrates the use of the
// \doxygen{otb}{FastICAImageFilter}.
-// This filter computes a Fast Independant Components Analysis transform.
+// This filter computes a Fast Independent Components Analysis transform.
//
// Like Principal Components Analysis, Independent Component Analysis
// \cite{jutten1991blind} computes a set of
// orthogonal linear combinations, but the criterion of Fast ICA is
// different: instead of maximizing variance, it tries to maximize
-// stastistical independance between components.
+// statistical independence between components.
//
// In the Fast ICA algorithm \cite{hyvarinen1999fast},
-// statistical independance is mesured by evaluating non-Gaussianity
+// statistical independence is measured by evaluating non-Gaussianity
// of the components, and the maximization is done in an iterative way.
// The first step required to use this filter is to include its header file.
@@ -68,9 +68,9 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
//
- // We start by defining the types for the images and the reader and
+ // We start by defining the types for the images, the reader, and
// the writer. We choose to work with a \doxygen{otb}{VectorImage},
- // since we will produce a multi-channel image (the principal
+ // since we will produce a multi-channel image (the independent
// components) from a multi-channel input image.
//
// Software Guide : EndLatex
@@ -108,8 +108,8 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
//
- // We then set the number of principal
- // components required as output. We can choose to get less PCs than
+ // We then set the number of independent
+ // components required as output. We can choose to get less ICs than
// the number of input bands.
//
// Software Guide : EndLatex
@@ -194,7 +194,7 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
// Figure~\ref{fig:FastICA_FILTER} shows the result of applying forward
- // and reverse FastICA transformation to a 8 bands Wordlview2 image.
+ // and reverse FastICA transformation to a 8 bands Worldview2 image.
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{FastICA-input-pretty.eps}
diff --git a/Examples/DimensionReduction/MNFExample.cxx b/Examples/DimensionReduction/MNFExample.cxx
index 466bc969f1fcef909d9550fd8465dfc86c13b406..0362784e04f67ff1a54b8bf0f1feb008c93099b4 100644
--- a/Examples/DimensionReduction/MNFExample.cxx
+++ b/Examples/DimensionReduction/MNFExample.cxx
@@ -81,7 +81,7 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
//
- // We start by defining the types for the images and the reader and
+ // We start by defining the types for the images, the reader, and
// the writer. We choose to work with a \doxygen{otb}{VectorImage},
// since we will produce a multi-channel image (the principal
// components) from a multi-channel input image.
@@ -231,7 +231,7 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
// Figure~\ref{fig:MNF_FILTER} shows the result of applying forward
- // and reverse MNF transformation to a 8 bands Wordlview2 image.
+ // and reverse MNF transformation to a 8 bands Worldview2 image.
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{MNF-input-pretty.eps}
diff --git a/Examples/DimensionReduction/MaximumAutocorrelationFactor.cxx b/Examples/DimensionReduction/MaximumAutocorrelationFactor.cxx
index 752826e4a7a9364c4e9b2b232135d4bb35633dea..32ea62cb42eb8812269677c4122141d21453c616 100644
--- a/Examples/DimensionReduction/MaximumAutocorrelationFactor.cxx
+++ b/Examples/DimensionReduction/MaximumAutocorrelationFactor.cxx
@@ -68,7 +68,7 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
//
// We can now declare the types for the reader. Since the images
- // can be vey large, we will force the pipeline to use
+ // can be very large, we will force the pipeline to use
// streaming. For this purpose, the file writer will be
// streamed. This is achieved by using the
// \doxygen{otb}{StreamingImageFileWriter} class.
diff --git a/Examples/DimensionReduction/NAPCAExample.cxx b/Examples/DimensionReduction/NAPCAExample.cxx
index 629c023b8d815a17978b182faa6a75ae85fa8486..ada3a8d1bbf1a86d1ae7ebaedae6574fa451066c 100644
--- a/Examples/DimensionReduction/NAPCAExample.cxx
+++ b/Examples/DimensionReduction/NAPCAExample.cxx
@@ -43,6 +43,7 @@
//
// The second Principal Component Analysis is then applied to the
// noise-whitened image, giving the Maximum Noise Fraction transform.
+// Applying PCA on noise-whitened image consists in ranking Principal Components according to signal to noise ratio.
//
// It is basically a reformulation of the Maximum Noise Fraction algorithm.
//
@@ -81,7 +82,7 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
//
- // We start by defining the types for the images and the reader and
+ // We start by defining the types for the images, the reader and
// the writer. We choose to work with a \doxygen{otb}{VectorImage},
// since we will produce a multi-channel image (the principal
// components) from a multi-channel input image.
@@ -232,7 +233,7 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
// Figure~\ref{fig:NAPCA_FILTER} shows the result of applying forward
- // and reverse NA-PCA transformation to a 8 bands Wordlview2 image.
+ // and reverse NA-PCA transformation to a 8 bands Worldview2 image.
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{napca-input-pretty.eps}
diff --git a/Examples/DimensionReduction/PCAExample.cxx b/Examples/DimensionReduction/PCAExample.cxx
index aa3bf14cf6cb0d628b3346c69fe8e72c0cdebdd1..02699b35569a46fa2ff4ea845880dd1d8fba02ee 100644
--- a/Examples/DimensionReduction/PCAExample.cxx
+++ b/Examples/DimensionReduction/PCAExample.cxx
@@ -97,7 +97,10 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
//
// The only parameter needed for the PCA is the number of principal
- // components required as output. We can choose to get less PCs than
+ // components required as output. Principal components are linear combination of input components
+ // (here the input image bands),
+ // which are selected using Singular Value Decomposition eigen vectors sorted by eigen value.
+ // We can choose to get less Principal Components than
// the number of input bands.
//
// Software Guide : EndLatex
@@ -157,7 +160,7 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
// Figure~\ref{fig:PCA_FILTER} shows the result of applying forward
- // and reverse PCA transformation to a 8 bands Wordlview2 image.
+ // and reverse PCA transformation to a 8 bands Worldview2 image.
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{input-pretty.eps}