From 0075054c72acd2dcd525a149d6df050c2d0c2910 Mon Sep 17 00:00:00 2001 From: Jordan Micah Bennett Date: Sun, 16 Jul 2017 20:40:25 -0500 Subject: [PATCH] Urgent typo fix suggestion MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit There is a typo in the "Introduction", section, under heading "PCA and Dimensionality Reduction", in the last sentence. CURRENT TEXT: "larger magnitude than others THAT the reduction..." SUGGESTED CORRECTION: "larger magnitude than others THEN the reduction..." --- principal_component_analysis.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/principal_component_analysis.ipynb b/principal_component_analysis.ipynb index efbf762..f96314a 100644 --- a/principal_component_analysis.ipynb +++ b/principal_component_analysis.ipynb @@ -215,7 +215,7 @@ "source": [ "Often, the desired goal is to reduce the dimensions of a $d$-dimensional dataset by projecting it onto a $(k)$-dimensional subspace (where $k\\;<\\;d$) in order to increase the computational efficiency while retaining most of the information. An important question is \"what is the size of $k$ that represents the data 'well'?\"\n", "\n", - "Later, we will compute eigenvectors (the principal components) of a dataset and collect them in a projection matrix. Each of those eigenvectors is associated with an eigenvalue which can be interpreted as the \"length\" or \"magnitude\" of the corresponding eigenvector. If some eigenvalues have a significantly larger magnitude than others that the reduction of the dataset via PCA onto a smaller dimensional subspace by dropping the \"less informative\" eigenpairs is reasonable.\n" + "Later, we will compute eigenvectors (the principal components) of a dataset and collect them in a projection matrix. Each of those eigenvectors is associated with an eigenvalue which can be interpreted as the \"length\" or \"magnitude\" of the corresponding eigenvector. If some eigenvalues have a significantly larger magnitude than others then the reduction of the dataset via PCA onto a smaller dimensional subspace by dropping the \"less informative\" eigenpairs is reasonable.\n" ] }, {