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 pca
Search options questions only
not deleted
user 41749
Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.
86
votes
3
answers
77k
views
What is the difference between ZCA whitening and PCA whitening?
I am confused about ZCA whitening and normal whitening (which is obtained by dividing principal components by the square roots of PCA eigenvalues). … As far as I know,
$$\mathbf x_\mathrm{ZCAwhite} = \mathbf U \mathbf x_\mathrm{PCAwhite},$$ where $\mathbf U$ are PCA eigenvectors.
What are the uses of ZCA whitening? …
- 13.1k
91
votes
4
answers
51k
views
What're the differences between PCA and autoencoder?
Both PCA and autoencoder can do demension reduction, so what are the difference between them? In what situation I should use one over another? …
- 13.1k