Principal component analysis: Difference between revisions
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* Analogously to the [[covariance matrix]] one can define a correlation matrix. What happens if you run SVD on the correlation matrix? | * Analogously to the [[covariance matrix]] one can define a correlation matrix. What happens if you run SVD on the correlation matrix? | ||
* multiple ways to look at PCA: | |||
** SVD on the covariance matrix (this is probably the same as one of the other interpretations) | |||
** maximum variance (see Bishop) | |||
** minimum-error (see Bishop) | |||
** the best linear compression-recovery of data to a lower dimension (see Shalev-Shwartz and Ben-David). Is this the same as minimum-error interpretation? | |||
Revision as of 03:18, 14 July 2018
Questions/things to explain
- Analogously to the covariance matrix one can define a correlation matrix. What happens if you run SVD on the correlation matrix?
- multiple ways to look at PCA:
- SVD on the covariance matrix (this is probably the same as one of the other interpretations)
- maximum variance (see Bishop)
- minimum-error (see Bishop)
- the best linear compression-recovery of data to a lower dimension (see Shalev-Shwartz and Ben-David). Is this the same as minimum-error interpretation?