Asked about PCA from scratch
Explain PCA.
An answer that works
Centre the data, then find the orthogonal directions along which it varies most, in order. Projecting onto the first few keeps as much variance as possible in fewer dimensions. In practice it is computed with an SVD of the centred data matrix.
Then they push further
Why centre the data first?
PCA looks for directions of variance around the mean. Without centring, the first component points towards the mean itself rather than along the spread.
When would you scale features before PCA?
When features are in different units, such as income and age. Otherwise the feature with the largest numbers dominates the variance and the components.
What gets marked up
Marked up for centring, the SVD connection, and knowing when to standardise.
Reading an answer is not being able to give one. The section that teaches this is PCA from scratch, 4 hours of Linear algebra.
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