Undergrad Eigenspectra and Empirical Orthogonal Functions

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SUMMARY

Eigenspectra and Empirical Orthogonal Functions (EOFs) are distinct concepts in data analysis. Eigenspectra refer to a spectrum of eigenvalues derived from a matrix, while EOFs serve as orthogonal basis functions for decomposing datasets. Both can be computed using the Singular Value Decomposition (SVD) method, but they are not interchangeable. The article titled "Accuracy of Spectrum Estimate in Fluorescence Spectral Microscopy with Spectral Filters" discusses the reconstruction of a sample's spectrum using these mathematical tools.

PREREQUISITES
  • Understanding of Singular Value Decomposition (SVD)
  • Familiarity with eigenvalues and eigenvectors
  • Knowledge of orthogonal functions
  • Basic principles of data reconstruction techniques
NEXT STEPS
  • Research the application of Singular Value Decomposition (SVD) in data analysis
  • Explore the mathematical foundations of eigenvalues and eigenvectors
  • Study the implementation of Empirical Orthogonal Functions (EOFs) in climate data analysis
  • Investigate the methods for reconstructing signals using eigenspectra
USEFUL FOR

Researchers, data analysts, and scientists involved in data reconstruction, particularly in fields such as climate science and microscopy, will benefit from this discussion.

ecastro
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Are the Eigenspectra (a spectrum of eigenvalues) and the Empirical Orthogonal Functions (EOFs) the same?

I have known that both can be calculated through the Singular Value Decomposition (SVD) method.

Thank you in advance.
 
Hi there. Eigenspectra is a spuctrum of eigenvalues. eigenvalues are values of a scalar
364442fc3d24dc6d566c98cfa307b1f0.png
so that [PLAIN]https://upload.wikimedia.org/math/3/6/4/364442fc3d24dc6d566c98cfa307b1f0.pngx=Bx. B is any given function. The eigenvalues of a matrix J can be found by finding det( J - [PLAIN]https://upload.wikimedia.org/math/3/6/4/364442fc3d24dc6d566c98cfa307b1f0.pngI). empirical orthogonal functions are a means of decomposing a dataset in terms of orthogonal basis functions. This is not what eigenspectra are.

This is a congenial problem for development for an anorthosite loving kid. i use wolfram alpha to help me with this problem. .
 
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From an article I have been reading, the eigenspectra they discuss can be calculated from a collection of data. They then use these eigenspectra to reconstruct some parts of the data.

##E = a_1 \hat{e}_1 + a_2 \hat{e}_2 + a_3 \hat{e}_3 + \cdots##,

where ##E## is the reconstructed signal, ##a_1, a_2, a_3, ...## are coefficients, and ##\hat{e}_1, \hat{e}_2, \hat{e}_3, ...## are what they call the eigenspectra. Isn't this also how the Empirical Orthogonal Functions are used to reconstruct a signal or data?
 
ecastro said:
From an article I have been reading,

Is the article available online ? What's the article about ?
 
The article's title is "Accuracy of Spectrum Estimate in Flourescence Spectral Microscopy with Spectral Filters". The article is about the reconstruction of a sample's spectrum.
 

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