Opened 11 years ago

Closed 11 years ago

#639 closed request (fixed)

PCA on a Kernel Matrix

Reported by: Peter Owned by: Peter
Priority: major Milestone: yat 0.7
Component: utility Version: trunk
Keywords: Cc:

Description

The PCA class works on a data matrix, i.e., it calculates the covariance matrix and diagonalizes it to find directions with most variance.

I need something slightly different because of two things. I wanna avoid holding the data matrix in memory because it is huge. Second, I have missing values in my data so I need to calculate the covariance matrix in a slightly different way than it is done in the PCA class (see AveragerPairWeighted).

I want to calculate the covariance matrix (or let's call it kernel matrix) outside the PCA class, feed it into a PCA class, calculate the principal components, and project my data onto say two largest principal components.

This doesn't really fit into the current PCA class, so I'm thinking of creating a new class PCA2 (?).

Thoughts?

Change History (1)

comment:1 Changed 11 years ago by Peter

Resolution: fixed
Status: newclosed

(In [2324]) new class KernelPCA. closes #639

Note: See TracTickets for help on using tickets.