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PCA compression thousand into hundred dimensional features
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If there's a compressed algorithm, there should be uncompressed algorithm to give data compression back to its original value
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With this, we can uncompressed the data reduction earlier, Ex hundred into thousand dimensional features(original_
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The graph above shows how we first make z1 projection line and give each example to be projected
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So how do we return the compressed back to original x?
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Left, original data, compressed then the right give the x approx
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Also called Reconstruction from the construct representation
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Quiz below , max retain should be equal to one. if more, than the data retain have over-variance over the original data.
SUMMARY
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These method shows us how we can convert the data reduction matrix z back to original x(or its original approximation)
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NEXT, mechanic how to use PCA well , how to choose k for reducing matrix z
