Relationship between SVD and PCA. How to use SVD to perform …
Jan 22, 2015 · Principal component analysis (PCA) is usually explained via an eigen-decomposition of the covariance matrix. However, it can also be performed via singular value …
pca - What are principal component scores? - Cross Validated
Jul 20, 2010 · Principal component scores are a group of scores that are obtained following a Principle Components Analysis (PCA). In PCA the relationships between a group of scores is …
Questions on PCA: when are PCs independent? why is PCA …
Principal components are guaranteed to be independent only if the data set is jointly normally distributed. Is the independence of principal components very important? How can I …
Loadings vs eigenvectors in PCA: when to use one or another?
Mar 29, 2015 · The value of n - i.e. the number of significant principal components to retain in the analysis, and hence the number of rows of L - is typically determined through the use of a …
What are the differences between Factor Analysis and Principal ...
Principal component scores are true component values; factor scores are only reasonable approximation to the indetermined true factor values. FA: roundup of the procedure To gather …
normalization - Why do we need to normalize data before …
I'm doing principal component analysis on my dataset and my professor told me that I should normalize the data before doing the analysis. Why? What would happen If I did PCA without …
Why are principal component scores uncorrelated? - Cross Validated
May 26, 2015 · Why are principal component scores uncorrelated? Ask Question Asked 10 years, 7 months ago Modified 10 years, 7 months ago
Is PCA followed by a rotation (such as varimax) still PCA?
Feb 8, 2015 · However, we started with principal components, which are not actually factors (as PCA is not the same as FA). So it would be weird to call it FA as well. Instead of debating …
Interpretation of biplots in principal components analysis
The left and bottom axes are showing [normalized] principal component scores; the top and right axes are showing the loadings. In general it assumes that two components explain a sufficient …
Why are principal components in PCA (eigenvectors of the …
Jan 1, 2015 · Why are principal components in PCA mutually orthogonal? I know that PCA can be calculated by eig(cov(X)), where X is centered. But I do not see why the eigenvectors should …