Principal Component Analysis - Lecture 8

Principal Component Analysis - Lecture 8

The Julia Programming Language via YouTube Direct link

Matrix rank

3 of 14

3 of 14

Matrix rank

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Principal Component Analysis - Lecture 8

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  1. 1 Introduction: Understanding data
  2. 2 Rank of a matrix
  3. 3 Matrix rank
  4. 4 Effect of noise
  5. 5 From images to data
  6. 6 Measuring data cloud "size" – using statistics
  7. 7 Measuring a "width" of a data set
  8. 8 Root-mean-square distance: Standard deviation
  9. 9 Correlated data
  10. 10 Rotating the axes
  11. 11 Rotating the data
  12. 12 Higher dimensions
  13. 13 What is the Singular-Value Decomposition (SVD)?
  14. 14 Rotations in 300 dimensions

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