Here we will discuss some of the most popular dimensionality reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE). - -
+Furthermore, we will start by looking at some simple preprocessing of the data which allow us to rescale the data. diff --git a/doc/pub/DimRed/html/DimRed-reveal.html b/doc/pub/DimRed/html/DimRed-reveal.html index ab3ab37a3..b6b87f510 100644 --- a/doc/pub/DimRed/html/DimRed-reveal.html +++ b/doc/pub/DimRed/html/DimRed-reveal.html @@ -172,8 +172,7 @@ turning an intractable problem into a tractable one.
Here we will discuss some of the most popular dimensionality reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE). - - +Furthermore, we will start by looking at some simple preprocessing of the data which allow us to rescale the data. diff --git a/doc/pub/DimRed/html/DimRed-solarized.html b/doc/pub/DimRed/html/DimRed-solarized.html index 0a5ebf217..d722569c4 100644 --- a/doc/pub/DimRed/html/DimRed-solarized.html +++ b/doc/pub/DimRed/html/DimRed-solarized.html @@ -154,8 +154,7 @@ turning an intractable problem into a tractable one.
Here we will discuss some of the most popular dimensionality reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE). - - +Furthermore, we will start by looking at some simple preprocessing of the data which allow us to rescale the data. diff --git a/doc/pub/DimRed/html/DimRed.html b/doc/pub/DimRed/html/DimRed.html index 2dfe0c080..aa6733c50 100644 --- a/doc/pub/DimRed/html/DimRed.html +++ b/doc/pub/DimRed/html/DimRed.html @@ -159,8 +159,7 @@ turning an intractable problem into a tractable one.
Here we will discuss some of the most popular dimensionality reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE). - - +Furthermore, we will start by looking at some simple preprocessing of the data which allow us to rescale the data. diff --git a/doc/pub/DimRed/ipynb/DimRed.ipynb b/doc/pub/DimRed/ipynb/DimRed.ipynb index 66c1a5305..c77b0241e 100644 --- a/doc/pub/DimRed/ipynb/DimRed.ipynb +++ b/doc/pub/DimRed/ipynb/DimRed.ipynb @@ -27,6 +27,7 @@ "\n", "Here we will discuss some of the most popular dimensionality\n", "reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE).\n", + "Furthermore, we will start by looking at some simple preprocessing of the data which allow us to rescale the data.\n", "\n", "\n", "\n", diff --git a/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz b/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz index 870791e0f..1c12d631a 100644 Binary files a/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz and b/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz differ diff --git a/doc/pub/DimRed/pdf/DimRed-minted.pdf b/doc/pub/DimRed/pdf/DimRed-minted.pdf index 52b23365d..35d6907b0 100644 Binary files a/doc/pub/DimRed/pdf/DimRed-minted.pdf and b/doc/pub/DimRed/pdf/DimRed-minted.pdf differ diff --git a/doc/src/DimRed/DimRed.do.txt b/doc/src/DimRed/DimRed.do.txt index baa6931a8..4fa0e2360 100644 --- a/doc/src/DimRed/DimRed.do.txt +++ b/doc/src/DimRed/DimRed.do.txt @@ -15,7 +15,7 @@ turning an intractable problem into a tractable one. Here we will discuss some of the most popular dimensionality reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE). - +Furthermore, we will start by looking at some simple preprocessing of the data which allow us to rescale the data. !eblock