A detailed and understandable summary of the PCS chapter for STA2005S. High quality pdf for printing. Contains examples for each concept.
Clearly explained so you can go straight into examples, instead of trying to understand the complicated lecture notes. Perfect for exam revision
Principal Component Analysis
Aim : PCA is a
dimensionality reduction technique ,
that identifies new
meaningful variables in dataset
Overview : PCA creates new variables ,
called principal components ,
that linear combination's of the variables
are
original
L
v
s
PCA Variables Each component explains PCA components arranged
uncorrelated percentage of in order of
are a
decreasing
variation in Oct dataset variance explained
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