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Singular Value Decomposition in Data Analysis

Learn the fundamentals of Singular Value Decomposition (SVD) and its application in data analysis, explained with an example dataset on object reflectance. Discover how SVD reveals basis functions and accounts for variance in the data.

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Singular Value Decomposition in Data Analysis

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  1. PCA Data

  2. PCA Data minus mean

  3. Eigenvectors

  4. Compressed Data

  5. Spectral Data

  6. Eigenvectors

  7. Fit Error – 2 Eigenfunctions

  8. Singular Value Decomposition (SVD) A matrix Amxn with m rows and n columns can be decomposed into A = USVT where UTU = I, VTV= I (i.e. orthogonal) and S is a diagonal matrix. If Rank(A) = p, then Umxp, Vnxp and Spxp OK, but what does this mean is English?

  9. SVD by Example A404x31= Keele Data on Reflectance of Natural Objects m = 404 rows of different objects n = 31 columns, wavelengths 400-700 nm in 10 nm steps Rank(A) = 31 means at least 31 independent rows

  10. SVD by Example A404x31 = U404x31 S31x31 VT31x31 UTU = I means dot product of two different columns of U equals zero. VTV = I means dot product of two different columns of V (rows of VT) equals zero.

  11. Basis Functions V31x31= Columns of V are basis functions that can be used to represent the original Reflectance curves.

  12. Basis Functions First column handles most of the variance, then the second column etc.

  13. Singular Values S31x31= The square of diagonal elements of S describe the variance accounted for by each of the basis functions.

  14. SVD Approximation A404x31 ~ U404xd Sdxd VTdx31 The original matrix can be approximated by taking the first d columns of U, reducing S to a d x d matrix and using the first d rows of VT.

  15. SVD Reconstruction Three Basis Functions Five Basis Functions

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