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”
