- PCA compression thousand into hundred dimensional features
- If there's a compressed algorithm, there should be uncompressed algorithm to give data compression back to its original value
- With this, we can uncompressed the data reduction earlier, Ex hundred into thousand dimensional features(original_

- The graph above shows how we first make z1 projection line and give each example to be projected
- So how do we return the compressed back to original x?
- Left, original data, compressed then the right give the x approx
- Also called Reconstruction from the construct representation
- Quiz below , max retain should be equal to one. if more, than the data retain have over-variance over the original data.

SUMMARY

- These method shows us how we can convert the data reduction matrix z back to original x(or its original approximation)
- NEXT, mechanic how to use PCA well , how to choose k for reducing matrix z