# Linear algebra: finding eigenvalues

Hello. I'm trying to do a principle component analysis of the following file of "good values" - to create a good data signature. I need to find the eigenvectors of each data series.

How do I derive the eigenvalues (or eigenvectors) and what are they from this data set? (If you could explain how to do it in matlab, that would be great - but it's not necessary for me to understand how it works)

Thank you.

"Does every line in the excel file represent a 16x16 matrix?"

I'm not sure. I would have to say yes, if it is the only way to get the eigenvalues. I was just given the data as a known good set of data and asked to do a principal component analysys (academic). To do a PCA, I need to find the eigenvalues, but my linear algebra book doesn't speak of how to do this with just a continuous stream of data.

In this case, are the eigenvalues just the averages, modes, or median values? Each line of data seems to represent one variable - so the matrix is a 60x1? If that's the case, is it even possible to derive eigen values? thanks.

--I'm willing to take an educated guess. Even if it's not the correct eigenvalues or the proper way to do principal component analysis, I'm open to ideas. I'm thinking about giving up and just using the mode of the data or the average as the characteristic values because I need to implement this into a nueral net. I'm trying to put together a (academic) prognostics presentation. Any opinion is greatly valued. Thanks again.

Â© BrainMass Inc. brainmass.com March 4, 2021, 6:17 pm ad1c9bdddfhttps://brainmass.com/math/linear-algebra/linear-algebra-finding-eigenvalues-38274

#### Solution Preview

See attached. Hope this helps.

Since the total number of columns is 256, it seems quite likely that each row represents a 16 x 16 matrix. Eigenvalues are only possible for square matrices, so this seems to be quite logical. In a nutshell, you have 15 matrices now, each 16 x 16 in dimension.

The decomposition of a square matrix A into ...

#### Solution Summary

The expert finds eigenvalues for linear algebra.