Jump to content

Talk:Kernel principal component analysis

Page contents not supported in other languages.
From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by Michael Hardy (talk | contribs) at 19:01, 22 April 2009 (WPStatistics). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.
WikiProject iconStatistics Unassessed
WikiProject iconThis article is within the scope of WikiProject Statistics, a collaborative effort to improve the coverage of statistics on Wikipedia. If you would like to participate, please visit the project page, where you can join the discussion and see a list of open tasks.
???This article has not yet received a rating on Wikipedia's content assessment scale.
???This article has not yet received a rating on the importance scale.
WikiProject iconRobotics Start‑class Mid‑importance
WikiProject iconThis article is within the scope of WikiProject Robotics, a collaborative effort to improve the coverage of Robotics on Wikipedia. If you would like to participate, please visit the project page, where you can join the discussion and see a list of open tasks.
StartThis article has been rated as Start-class on Wikipedia's content assessment scale.
MidThis article has been rated as Mid-importance on the project's importance scale.

On redirection to SVM

What is the relationship between kernel PCA and SVMs? I don't see any direct connection. //Memming 15:50, 17 May 2007 (UTC)[reply]

There is no relation, this is a common mistake. Not every kernel points to an SVM. Kernel is a more common thing in math.
Then I'll break the redirection to SVM. //Memming 12:00, 21 May 2007 (UTC)[reply]

Data reduction in the feature space

In the litterature, I found the way to center the input data in the feature space. Nevertheless, I never found a way to reduce the data in the feature space, so if anyone has knowledge about it, I would be glad if he could explain that toppic here or give few links