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Singular Value Decomposition versus Principal Component Analysis - Revision history
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http://www.convexoptimization.com/wikimization/index.php?title=Singular_Value_Decomposition_versus_Principal_Component_Analysis&diff=3232&oldid=prev
Ranjelin at 03:43, 18 September 2018
2018-09-18T03:43:22Z
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>Terminology like <i>variance</i> of principal components (PCs) can be found here:</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>Terminology like <i>variance</i> of principal components (PCs) can be found here:</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>[https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca Relationship between SVD and PCA].</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>[https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca Relationship between SVD and PCA].</div></td></tr>
<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div><br><i>Standard deviation</i> <del style="color: red; font-weight: bold; text-decoration: none;">is square root of </del>variance.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><br<ins style="color: red; font-weight: bold; text-decoration: none;">><b>(</b</ins>><i>Standard deviation</i> <ins style="color: red; font-weight: bold; text-decoration: none;">squared equals </ins>variance.<ins style="color: red; font-weight: bold; text-decoration: none;"><b>)</b></ins></div></td></tr>
<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div><br>Sign of principal component <del style="color: red; font-weight: bold; text-decoration: none;">vectors are </del>not unique.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><br>Sign of principal component <ins style="color: red; font-weight: bold; text-decoration: none;">vector is </ins>not unique.</div></td></tr>
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Ranjelin
http://www.convexoptimization.com/wikimization/index.php?title=Singular_Value_Decomposition_versus_Principal_Component_Analysis&diff=3231&oldid=prev
Ranjelin at 03:39, 18 September 2018
2018-09-18T03:39:17Z
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>[https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca Relationship between SVD and PCA].</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>[https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca Relationship between SVD and PCA].</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div><br><i>Standard deviation</i> is square root of variance.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div><br><i>Standard deviation</i> is square root of variance.</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><br>Sign of principal component vectors are not unique.</div></td></tr>
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Ranjelin
http://www.convexoptimization.com/wikimization/index.php?title=Singular_Value_Decomposition_versus_Principal_Component_Analysis&diff=3230&oldid=prev
Ranjelin at 20:28, 16 September 2018
2018-09-16T20:28:01Z
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div></pre></div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div></pre></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"></td></tr>
<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div><i>coef, score, latent</i> definitions from <del style="color: red; font-weight: bold; text-decoration: none;">Matlab </del></div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><i>coef, score, latent</i> definitions from <ins style="color: red; font-weight: bold; text-decoration: none;"> </ins></div></td></tr>
<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>[https://www.mathworks.com/help/stats/pca.html pca()]</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>[https://www.mathworks.com/help/stats/pca.html <ins style="color: red; font-weight: bold; text-decoration: none;">Matlab </ins>pca()]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>command.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>command.</div></td></tr>
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Ranjelin
http://www.convexoptimization.com/wikimization/index.php?title=Singular_Value_Decomposition_versus_Principal_Component_Analysis&diff=3229&oldid=prev
Ranjelin at 20:27, 16 September 2018
2018-09-16T20:27:06Z
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>from <i>SVD meets PCA</i> slide by Cleve Moler.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>from <ins style="color: red; font-weight: bold; text-decoration: none;">[https://www.mathworks.com/videos/the-singular-value-decomposition-saves-the-universe-1481294462044.html </ins><i>SVD meets PCA</i><ins style="color: red; font-weight: bold; text-decoration: none;">] </ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>slide <ins style="color: red; font-weight: bold; text-decoration: none;">[17:46] </ins>by Cleve Moler.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>“''The Wikipedia pages on SVD and PCA are quite good and contain a number of useful links, although not to each other.''”</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>“''The Wikipedia pages on SVD and PCA are quite good and contain a number of useful links, although not to each other.''”</div></td></tr>
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Ranjelin
http://www.convexoptimization.com/wikimization/index.php?title=Singular_Value_Decomposition_versus_Principal_Component_Analysis&diff=3228&oldid=prev
Ranjelin at 20:21, 16 September 2018
2018-09-16T20:21:21Z
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>from <i>SVD meets PCA</i><del style="color: red; font-weight: bold; text-decoration: none;">, </del>slide by Cleve Moler</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>from <i>SVD meets PCA</i> slide by Cleve Moler<ins style="color: red; font-weight: bold; text-decoration: none;">.</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>“''The Wikipedia pages on SVD and PCA are quite good and contain a number of useful links, although not to each other.''”</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>“''The Wikipedia pages on SVD and PCA are quite good and contain a number of useful links, although not to each other.''”</div></td></tr>
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Ranjelin
http://www.convexoptimization.com/wikimization/index.php?title=Singular_Value_Decomposition_versus_Principal_Component_Analysis&diff=3227&oldid=prev
Ranjelin at 20:19, 16 September 2018
2018-09-16T20:19:12Z
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>Terminology like <i>variance</i> of principal components (PCs) can be found here:</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>Terminology like <i>variance</i> of principal components (PCs) can be found here:</div></td></tr>
<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>[https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca Relationship between SVD and PCA]</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>[https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca Relationship between SVD and PCA]<ins style="color: red; font-weight: bold; text-decoration: none;">.</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div><br><i>Standard deviation</i> is square root of variance.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div><br><i>Standard deviation</i> is square root of variance.</div></td></tr>
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Ranjelin
http://www.convexoptimization.com/wikimization/index.php?title=Singular_Value_Decomposition_versus_Principal_Component_Analysis&diff=3226&oldid=prev
Ranjelin at 20:18, 16 September 2018
2018-09-16T20:18:38Z
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>command.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>command.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"></td></tr>
<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>Terminology like <i>variance <del style="color: red; font-weight: bold; text-decoration: none;">of principal components</del></i> can be found here:</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>Terminology like <i>variance</i> <ins style="color: red; font-weight: bold; text-decoration: none;">of principal components (PCs) </ins>can be found here:</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>[https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca Relationship between SVD and PCA]</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>[https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca Relationship between SVD and PCA]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div><br><i>Standard deviation</i> is square root of variance.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div><br><i>Standard deviation</i> is square root of variance.</div></td></tr>
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Ranjelin
http://www.convexoptimization.com/wikimization/index.php?title=Singular_Value_Decomposition_versus_Principal_Component_Analysis&diff=3225&oldid=prev
Ranjelin at 05:20, 16 September 2018
2018-09-16T05:20:41Z
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>command.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>command.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"></td></tr>
<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div><del style="color: red; font-weight: bold; text-decoration: none;">Good explanation of terminology </del>like <i>variance of principal components</i> can be found here:</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins style="color: red; font-weight: bold; text-decoration: none;">Terminology </ins>like <i>variance of principal components</i> can be found here:</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>[https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca Relationship between SVD and PCA]</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>[https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca Relationship between SVD and PCA]</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><br><i>Standard deviation</i> is square root of variance.</div></td></tr>
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Ranjelin
http://www.convexoptimization.com/wikimization/index.php?title=Singular_Value_Decomposition_versus_Principal_Component_Analysis&diff=3224&oldid=prev
Ranjelin at 05:15, 16 September 2018
2018-09-16T05:15:13Z
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>coef = V(:,1:rho).*sense2</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>coef = V(:,1:rho).*sense2</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div></pre></div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div></pre></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><i>coef, score, latent</i> definitions from Matlab </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>[https://www.mathworks.com/help/stats/pca.html pca()]</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>command.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>Good explanation of terminology like <i>variance of principal components</i> can be found here:</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>Good explanation of terminology like <i>variance of principal components</i> can be found here:</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>[https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca Relationship between SVD and PCA]</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>[https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca Relationship between SVD and PCA]</div></td></tr>
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Ranjelin
http://www.convexoptimization.com/wikimization/index.php?title=Singular_Value_Decomposition_versus_Principal_Component_Analysis&diff=3223&oldid=prev
Ranjelin at 05:11, 16 September 2018
2018-09-16T05:11:55Z
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>coef = V(:,1:rho).*sense2</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>coef = V(:,1:rho).*sense2</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div></pre></div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div></pre></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>Good explanation of terminology like <i>variance of principal components</i> can be found here:</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>[https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca Relationship between SVD and PCA]</div></td></tr>
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Ranjelin