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I have X = [25, 2000] i.e. 25 subjects and 2000 values (i.e. each subject has a spectrogram that is reduced to 2000 values).

My goal is to reduce from 25 subjects to 1 or 2 "subjects" that best explains the data across the group.

If I do [u,s,v]=svd(X) (in matlab) or [u1,s1,v1]=svd(X')

What would be 1st and 2nd principle components?

Is it just columns of v (in first case) or columns of u (in transposed case)

OR do I have to do T = vX or T=uX and then the 1st and 2nd row of this?

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  • $\begingroup$ You might be better suited computing svd(X'*X), which is what is more commonly called "PCA". See my answer here for more detail: math.stackexchange.com/a/612115/31475 $\endgroup$ – Emily Feb 26 '14 at 19:27
  • $\begingroup$ Hi, In laymans terms can you please explain this a bit more. I have not been able to find a good explanation for when one should use the svd (cov(X)) vs svd (X). In my case, I tried both and when I use this [U,SS,V] = svd(YY'); W = sqrt(SS); w = diag(W); L = diag(1./w)*U'; T = LY; Rows of T = Columns of V (in [u,s,v=svd(Y)] $\endgroup$ – susan Feb 26 '14 at 20:31
  • $\begingroup$ I mis-typed my earlier response. I wanted to say "you might be better suited computing svd(X)'*svd(X) than svd(X'*X). Basically, SVD and PCA are the same things. In PCA, you compute the eigenvalues of $X^TX$ or $XX^T$, depending on how your data are arranged. However, computing this directly can induce numerical issues. However, if $X = U\Sigma V^T$, then $XX^T = U\Sigma V^T V \Sigma^T U^T = U\Sigma^2 U^T$. This is like the eigenvalue decomposition of $X^XT = V\Lambda V^T$; in fact, the singular values of $X$ are the square roots of the eigenvalues of the covariance matrix. $\endgroup$ – Emily Feb 26 '14 at 20:51
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If the SVD of $X$ is $X=USV^\top$, then the SVD of $X^\top$ is just the transpose of the prior factorization, $X^\top=VSU^\top$ or $U_1=V$, $S_1=S$ and $V_1=U$.

The principal components of this approach are the singular vectors with the largest singular values. In the implementations, the diagonal matrix $S$ contains the singular values sorted from largest to smallest, so that you only have to consider the first two components. If $X$ has format $25\times 2000$, then the columns of the $25\times 25$ matrix $U$ contain the singular vectors you are interested in.


Update

PCA was originally invented in mechanics to study the kinematics of rigid bodies, for instance the rotation and nutation and oscillations of planets. The idea there is that these kinematics are the same as an ellipsoid that is aligned and shaped according to the principal components of the mass distribution. Any movement of a rigid body can be described as the movement of its center of mass and a rotation around that center of mass.


If the data is not shifted so that the center of mass is the origin, for instance if in 2D all points are clustered around $(1,1)$, then the principal component of the data set will be close to this point $(1,1)$. But to get that point, one could just as well only have computed the center of mass or mean value of all data points. To get the information about the shape of the cluster out of the SVD, you have to subtract the center of mass.

If that is what you mean by 'subtracting the baseline' then all is well in that regard. But still, the application of SVD makes the most sense if you can say that if you flip the sign of an input vector, then this could have reasonable come as well from a measurement in the experiment.


The result of the SVD can be written as $$ X=\sum_{k=1}^r u_k\sigma_k v_k^\top. $$ If one pair of $(u_k,v_k)$ is replaced by $(-u_k,-v_k)$ then noting changes in the sum, the sign change cancels between both factors.

To get the data set of person $j$ out of the matrix $X$ one has to select row $j$ of $X$ as $e_j^\top X$. Now if $X$ gets compressed by using only the terms for the first or first two singular values in the SVD, the approximation of data $j$ set will be $$ e_j^\top X=\sum_{k=1}^2 (e_j^\top u_k)(\sigma_kv_k)^\top =\sum_{k=1}^2 U_{jk}(\sigma_kv_k)^\top. $$ Again, any sign changes in $v_k$ in the computation of the SVD are balanced by sign changes in the coefficients $e_j^\top u_k=U_{jk}$.

One heuristic to make the sign definitive could be to make sure that the entry with largest absolute value in every vector $u_k$ is positive.

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  • $\begingroup$ Hi! Please bear with me since these terms are a bit confusing. 1) Since I start out with spectrograms for 25 subjects, what I would like to see is 1st "spectrogram" - does this make sense? i.e. if I reduced from 25 spectrograms to 1 or 2, I'd like to see that. I'm assuming that is the columns of U1 or V? $\endgroup$ – susan Feb 26 '14 at 20:23
  • $\begingroup$ Right, the leading spectrogram, i.e., the singular vector of length 2000, is then the first column of $V$. Or $U_1$. $\endgroup$ – LutzL Feb 26 '14 at 20:29
  • $\begingroup$ 2) I while the rows of U1 = rows of V, the columns of U1 = sign inverse (columns of V). What is "correct" . One "looks" more like the data I input in but I want to understand this. $\endgroup$ – susan Feb 26 '14 at 20:30
  • $\begingroup$ That is normal, the singular vectors are only determined up to a factor of modulus 1, in the real case up to a sign. If you have a sign flip in one column between $V$ and $U_1$, then there is at the same time a sign flip in the corresponding column between $U$ and $V_1$. $\endgroup$ – LutzL Feb 26 '14 at 20:32
  • $\begingroup$ ok, so what do I use :) ? this is a bit important since it is baseline subtracted spectrogram and the sign determines suppression vs increase. $\endgroup$ – susan Feb 26 '14 at 20:33

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