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 179077
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.
0
votes
1
answer
338
views
In case of semi-supervised data, and PCA pre-processing, should I pre-process all the data o...
suppose I have labeled data and unlabeled data, should I do a PCA process on all the data, and then feed the labeled data through a classifier? … Or take only the labeled data through the PCA process and then to the classifier? …
- 123
1
vote
2
answers
671
views
How to plot High Dimensional supervised K-means on a 2D plot chart
I was thinking about using PCA and transform the data from 80D to 2D, but It only retain 40% of the variance!
Is this a good approach for the problem?
If so, does 40% suffice? …
- 123