[R] Confused by SVD and Eigenvector Decomposition in PCA
Feng Zhang
f0z6305 at labs.tamu.edu
Sat Feb 8 04:17:03 CET 2003
I used Matlab to do this case study.
>x = randn(200,3); %%generating a 200x3 Gaussian matrix
>[a,b,c]=svd(x); %%SVD composition
>S=diag(b)
S =[15.6765 14.8674 13.4016]'
>S(1)^2/sum(S.^2);
0.3802
>ZeroedX = X - repmat(mean(X),200,1); %%ZeroedX is now zero centered data
>C = cov(ZeroedX); %%Covariance matrix of ZeroedX
>[U,L] = eig(C); %% Eigen decompostion of C
> SE = diag(L);
[0.8918 1.1098 1.2337]'
>SE(1)/sum(SE)
0.3813
This is the case that I was confused by.
Fred
----- Original Message -----
From: "Liaw, Andy" <andy_liaw at merck.com>
To: "'Feng Zhang'" <f0z6305 at labs.tamu.edu>
Sent: Friday, February 07, 2003 6:25 PM
Subject: RE: [R] Confused by SVD and Eigenvector Decomposition in PCA
> I've already shown you one example. If that's not enough, here's another
> one:
>
> > set.seed(1)
> > x <- matrix(runif(1e3), 50, 20)
> > La.eigen(crossprod(x))$value
> [1] 258.5242317 9.3638224 8.7213839 7.7425270 6.5057190
6.2719056
> [7] 5.6582657 4.5002047 4.2289555 3.9098726 3.7172642
3.2826449
> [13] 2.8758329 2.6907474 2.3300505 1.9700120 1.3191512
1.0228788
> [19] 0.8883083 0.5883287
> > La.svd(x)$d^2
> [1] 258.5242317 9.3638224 8.7213839 7.7425270 6.5057190
6.2719056
> [7] 5.6582657 4.5002047 4.2289555 3.9098726 3.7172642
3.2826449
> [13] 2.8758329 2.6907474 2.3300505 1.9700120 1.3191512
1.0228788
> [19] 0.8883083 0.5883287
>
> Where's your example of this not working?
>
> Andy
>
>
> > -----Original Message-----
> > From: Feng Zhang [mailto:f0z6305 at labs.tamu.edu]
> > Sent: Friday, February 07, 2003 12:07 PM
> > To: antonio rodriguez; R-Help
> > Subject: Re: [R] Confused by SVD and Eigenvector Decomposition in PCA
> >
> >
> > Thanks for those replies.
> >
> > But I tested several cases, and found the two
> > percentage from SVD and EVD are not
> > the same.
> > So how to explain the difference and which
> > one should be the right one for use
> > in PCA?
> >
> >
> > ----- Original Message -----
> > From: "antonio rodriguez" <arv at ono.com>
> > To: "Feng Zhang" <f0z6305 at labs.tamu.edu>; "R-Help"
> > <r-help at stat.math.ethz.ch>
> > Sent: Friday, February 07, 2003 2:36 AM
> > Subject: Re: [R] Confused by SVD and Eigenvector Decomposition in PCA
> >
> >
> > > Hi Feng,
> > >
> > > AFIK SVD analysis provides a one-step method for computing all the
> > > components of the eigen value problem, without the need to
> > compute and
> > > store big covariance matrices. And also the resulting
> > decomposition is
> > > computationally more stable and robust.
> > >
> > > Cheers,
> > >
> > > Antonio Rodriguez
> > >
> > >
> > > ----- Original Message -----
> > > From: "Feng Zhang" <f0z6305 at labs.tamu.edu>
> > > To: "R-Help" <r-help at stat.math.ethz.ch>
> > > Sent: Thursday, February 06, 2003 7:03 PM
> > > Subject: [R] Confused by SVD and Eigenvector Decomposition in PCA
> > >
> > >
> > > > Hey, All
> > > >
> > > > In principal component analysis (PCA), we want to know how many
> > > percentage
> > > > the first principal component explain the total variances
> > among the
> > > data.
> > > >
> > > > Assume the data matrix X is zero-meaned, and
> > > > I used the following procedures:
> > > > C = covriance(X) %% calculate the covariance matrix;
> > > > [EVector,EValues]=eig(C) %%
> > > > L = diag(EValues) %%L is a column vector with eigenvalues as the
> > > elements
> > > > percent = L(1)/sum(L);
> > > >
> > > >
> > > > Others argue using Sigular Value Decomposition(SVD) to
> > > > calculate the same quantity, as:
> > > > [U,S,V]=svd(X);
> > > > L = diag(S);
> > > > L = L.^2;
> > > > percent = L(1)/sum(L);
> > > >
> > > >
> > > > So which way is the correct method to calculate the percentage
> > > explained by
> > > > the first principal component?
> > > >
> > > > Thanks for your advices on this.
> > > >
> > > > Fred
> > > >
> > > > ______________________________________________
> > > > R-help at stat.math.ethz.ch mailing list
> > > > http://www.stat.math.ethz.ch/mailman/listinfo/r-help
> > >
> > >
> > > ---
> > >
> > > ______________________________________________
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> > > http://www.stat.math.ethz.ch/mailman/listinfo/r-help
> >
> > ______________________________________________
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> >
>
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