Comments - Introduction to Principal Component Analysis - Data Science Central2019-03-22T18:14:06Zhttps://www.datasciencecentral.com/profiles/comment/feed?attachedTo=6448529%3ABlogPost%3A554953&xn_auth=noPCA has many applications in…tag:www.datasciencecentral.com,2017-05-26:6448529:Comment:5671992017-05-26T09:03:31.448ZZhongmin Luohttps://www.datasciencecentral.com/profile/ZhongminLuo
<p>PCA has many applications in finance industry; I have plan to write a paper in this area.This paper has used PCA to detect and mitigate the risk of highly correlated feature variables (which is common in finance): <a href="https://ssrn.com/abstract=2967184" target="_blank">https://ssrn.com/abstract=2967184</a></p>
<p>PCA has many applications in finance industry; I have plan to write a paper in this area.This paper has used PCA to detect and mitigate the risk of highly correlated feature variables (which is common in finance): <a href="https://ssrn.com/abstract=2967184" target="_blank">https://ssrn.com/abstract=2967184</a></p> Well, PCA is not "based on" r…tag:www.datasciencecentral.com,2017-04-27:6448529:Comment:5551082017-04-27T02:32:24.964ZLance Norskoghttps://www.datasciencecentral.com/profile/LanceNorskog
<p>Well, PCA is not "based on" rotation matrices (it's from 1900 after all) but "an example of" a rotation matrix.</p>
<p>Well, PCA is not "based on" rotation matrices (it's from 1900 after all) but "an example of" a rotation matrix.</p> PCA became clear to me when I…tag:www.datasciencecentral.com,2017-04-27:6448529:Comment:5553012017-04-27T02:31:12.194ZLance Norskoghttps://www.datasciencecentral.com/profile/LanceNorskog
<p>PCA became clear to me when I realized that it is based on the computer graphics tool called a rotation matrix.</p>
<p><a href="https://en.wikipedia.org/wiki/Rotation_matrix" target="_blank">https://en.wikipedia.org/wiki/Rotation_matrix</a></p>
<p></p>
<p>A rotation matrix rotates a shape in 2-space or 3-space, keeping the same area (volume). Take a 100x3 matrix, consider the rows as points in 3-space, and the columns as dimensions x, y, and z. PCA creates a rotation matrix which gives the…</p>
<p>PCA became clear to me when I realized that it is based on the computer graphics tool called a rotation matrix.</p>
<p><a href="https://en.wikipedia.org/wiki/Rotation_matrix" target="_blank">https://en.wikipedia.org/wiki/Rotation_matrix</a></p>
<p></p>
<p>A rotation matrix rotates a shape in 2-space or 3-space, keeping the same area (volume). Take a 100x3 matrix, consider the rows as points in 3-space, and the columns as dimensions x, y, and z. PCA creates a rotation matrix which gives the largest distance in (x,y) between points in (x,y,z). This gives the most dramatic visualization when you plot in 2D using (x,y) and dropping (z).</p>