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Principle component analysis
•Suppose each
data point is N-dimensional
–Same
procedure applies:
–The
eigenvectors of A define a new coordinate system
•eigenvector
with largest eigenvalue captures the most variation among training vectors x
•eigenvector
with smallest eigenvalue has least variation
–We can
compress the data by only using the top few eigenvectors
•corresponds
to choosing a “linear subspace”
–represent
points on a line, plane, or “hyper-plane”
