I am planning to work on NIPALS after the 0.15 sklearn release - there are
several good papers I will try to work with and implement.
Simple, high level description:
http://www.vias.org/tmdatanaleng/dd_nipals_algo.html
Simple MATLAB (will start with this first likely):
Sorry - in my previous email I should have said that I will be working on
NIPALS PCA . As you correctly note PLS(algorithm=nipals) is solving the
PLS objective, which is different than the PCA objective.
For anyone who is curious, see
Hi,
I was looking to the code in the cross-decomposition section. In PLS the
PCs are computed in order to maximize the correlation between X and y and
not to maximize the covariance of X.
def _nipals_twoblocks_inner_loop(X, Y, mode=A, max_iter=500, tol=1e-06,
Hi,
Just to summarize the situation and to avoid confusion.
There are mainly two things where I was focusing my attention.
1 - Nipals PCA (
http://en.wikipedia.org/wiki/Principal_component_analysis#The_NIPALS_method
)
This is a good alternative to SVD and it is much faster in situations where
On Mon, Jun 02, 2014 at 12:27:34AM +0100, Luca Puggini wrote:
This is a good alternative to SVD and it is much faster in situations where we
have a lot of variables and we are interested only in a small number of
components.
This is a well known and tested algorithm and I was actually
