Re: a principal component analysis question



On 29 May 2005 22:52:18 -0700, "Yiyu" <shenyiyu@xxxxxxxxx> wrote:

>The question is following: the simplest case: suppose I have k
>variables, x(1) - x(k), I do a principle component analysis and find
>the first 3 principle components: z(1) - z(3) explain most of the total
>variation. But I suspect x(1) - x(k) are actually of two different
>groups, and for the 1st group, z(1) and z(2) alone are enough to
>explain most of its total variation, for the 2nd group, z(1) and z(3)
>are enough to explain most of its total variation. So instead of
>express the whole group in a 3-dimension space, it will do better to
>express the data with two 2-dimension planes, and these two planes are
>orthogonal to each other. H
>
>Hope the question is clear enough. Do you know any previous research
>efforts related to similar problems? and any references?
>
>Thank you very much.
>
>Yiyu

Are the 1st and 2nd groups defined before the PCA was run? For
example, are they groups such as male and female?

If so then it sounds as if you should be doing a canonical variates
analysis on the two groups and interpreting that. I have seen this
procedure characterised as the 'PCA' you do when your primary interest
is groups, not individuals.

.



Relevant Pages

  • a principal component analysis question
    ... variables, x- x, I do a principle component analysis and find ... But I suspect x- xare actually of two different ... are enough to explain most of its total variation. ... express the data with two 2-dimension planes, ...
    (sci.stat.math)
  • Re: PCA without Mean Centering
    ... In the usual context of PCA, you want to mean center your data, because otherwise the first component does not really describe the largest direction of variation in the data, but rather it tends to describe the mean of the data, or at least some combination of the mean and the direction of largest variation. ... Imagine a cloud of points in 3-space, and imagine a vector pointing from the origin to the center of the cloud. ...
    (comp.soft-sys.matlab)
  • Re: eigenvalues of the covarience matrix (princomp)
    ... ZSCORE centers each column to have zero mean, and normalizes each column to have unit variance. ... There's limited use in doing PCA on non-centered data, because the first component will typically describe the mean of the data, and that's not what most people want out of PCA. ... My own opinion is that doing PCA on unstandardized variables implies that you think that the scales on which the different variables are measured are somehow "natural" and "comparable", in the sense that variation of some absolute magnitude in one variable is no more or less important than the same amount of absolute variation in another variable. ...
    (comp.soft-sys.matlab)
  • Re: a principal component analysis question
    ... Yiyu wrote: ... the 2 groups can not be identified before the PCA was run. ... component explains little variation. ... Prev by Date: ...
    (sci.stat.math)