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”
