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Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.

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, the one that is closest to the centroid mean. However, I was wondering whether something like this could also be achieved using the PCA. We can use PCA to reduce the dimensionality of the data to a … the data matrix (treating the observations as variables) and solving PCA (SVD).Would the coefficients in the PC then tell us the contribution of each observation to a particular PC? Do similar …
asked Aug 25 '13 by means-to-meaning
5
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might be confused about the fact that you use the PCA to obtain a set of eigenvectors (eigenfaces) $I = \{u_1, u_2, \ldots, u_D\}$ of the covariance matrix $X^TX$, where each $u_i \in \mathbb{R}^{N}$. You …
answered Oct 29 '13 by means-to-meaning
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concerned, I have not heard about analyzing the change of variable importance in PCA (though I no PCA expert!). In either case, I would imagine that you would probably have to account for the … it, but unless you find some well documented method that mathematically proves that it means anything, it is vague at best. In your case, I would rather cross-validate the PCA in both datasets and check whether the differences are not purely random. …
answered Jul 5 '12 by means-to-meaning