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 not deleted
user 252822
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.
4
votes
0
answers
41
views
Productionize (applied) PCA to detect outlier etc. long term with new data?
I was wondering how one could use PCA in e.g. a dashboard for non Subject Matter Expert.
For example, you are quite certain that 2 PCs are sufficient based on the current data. …
0
votes
PCA explained variance and clustering
PCA does only make sense when correlations exist.
It is a variance-maximizing orthogonal projection. …