what does eigenvalues expres in the covariance matrix?
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is there a relationship between a covariance matrix and eigenvalues? like an example
Let us consider a 321 × 261 image dimention 321 × 261 = 83781. We have only 32 observations and 83781 unknowns then we have a matrix of (32 row X 83781 column)
then we will calculate the covariance matrix (32 X 32) so we get 32 eigenvalues the question is: does these eigenvalues express the 32 images? or there is no any relationship between eigenvalues and images
thanks for you,
Kye Taylor on 23 Apr 2013
Edited: Kye Taylor on 23 Apr 2013
Long story short: The eigenvalues of the covariance matrix encode the variability of the data in an orthogonal basis that captures as much of the data's variability as possible in the first few basis functions (aka the principle component basis).
For example, this code creates an ellipse, whos major axis is the x-axis, and whos minor axis is the y-axis.
t = linspace(0,2*pi,256);
data = [cos(t);0.2*sin(t)];
Now, compute the variance of the data's coordinates
% tranpose to get variance down each column
% computes variance of each coordinate of the data
v = var(data')
Finally, observe the eigenvalues of the covariance matrix are equal to the variance of the data's coordinates
% need to transpose since input to cov must have
% rows = observations
% columns = variables
l = eig(cov(data'))
In other words, v and l contain the same values. The order may be different because the eig function returns eigenvalues in no particular order.
Note that if the data is rotated so that the major and minor axes are no longer the x and y axes, then var(data') no longer computes the variance about the coordinates from their principle axes, but eig(cov(data')) does automatically since the eigenvectors are principle components.
% rotate everything
r = [cos(pi/4),-sin(pi/4);sin(pi/4),cos(pi/4)]; % rotation matrix
rData = r*data;
plot(rData(1,:),rData(2,:),'r.') % plot rotated data
vr = var(rData') % different
lr = eig(cov(rData')) % should be same as l
More Answers (1)
Shashank Prasanna on 23 Apr 2013
Edited: Shashank Prasanna on 23 Apr 2013
Essentially what you are describing are the principal components of your data.
Its a popularly used dimensionality reduction technique, for example to make your image smaller such that it still retains most of its variance.
The PCA command in MATLAB does all this for you directly.