Why is the first factor in Kernel Principal Component Analysis a very big number?

1 view (last 30 days)
Hi everyone,
I am using KPCA with the function provided by Ambarish Jash (https://uk.mathworks.com/matlabcentral/fileexchange/27319-kernel-pca). The first component extracted from the matrix is significantly different than the others. Hence, when I try to regress the component with a simple ols I get a message regarding the singularity of the matrix. A sample code could be some thing like:
x = randn(555,10);
[Kcomponents]=kernelpca(x',10);
Kcomponents= Kcomponents';
y=randn(555,1);
y= y(2:end);
KCOM= Kcomponents(1:end-1,1:2);
T=size(y,1);
results_K=ols(y,[ones(T,1) KCOM]);
However, if I reduce the dimensions of x from 555x10 to 100x10, I do not get the same message. Does anyone know why this happens? I would appreciate any help. Thanks in advance

Answers (0)

Categories

Find more on Image Processing and Computer Vision in Help Center and File Exchange

Tags

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!