Thursday, September 15, 2011

9/15/11

In PCA (principle component analysis), we are trying to reduce as much
as possible the number of dimensions while still retaining as much
information as possible. In other words, we are finding the axes that
show as much spread of the data as possible. The details have not yet
been discussed, but it turns out that the eigenvectors determine the
dimensions, and the corresponding eigenvalues determine the importance
of the dimension.

Andree