Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with kernel-trick
Search options not deleted
user 364412
Kernel methods are used in machine learning to generalize linear techniques to nonlinear situations, especially SVMs, PCA, and GPs. Not to be confused with [kernel-smoothing], for kernel density estimation (KDE) and kernel regression.
0
votes
How to derive the three matrices of SVD from eigenvalue decomposition in Kernel PCA?
Based on @whuber's answer it's simply:
$$
\mathbf{V}=\mathbf{X^TU\Sigma^{-1}}
$$
- 23
1
vote
1
answer
101
views
How to derive the three matrices of SVD from eigenvalue decomposition in Kernel PCA?
Kernel PCA is usually done via eigenvalue decomposition of the Kernel Matrix $\mathbf{K}$ and standard PCA via SVD of the input $\mathbf{X}$.
In standard PCA as far as I know we can derive $\mathbf{S …
- 23