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 46221
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
626
views
Doing PCA with $m$ vectors in $d$ dimensions and then plotting only $n$ vectors, when $n<d<m$
( [.. my vectors ..] )
pca = PCA(data)
res = pca.Y
matplotlib.pyplot.scatter(res[:,0], res[:,1])
But PCA just works when the number of vectors is bigger than the dimensions of the vectors: N > D. … Or would it be better to use every input vector multiple times (in my example 11 times) to do the PCA transformation?
Is PCA in such a case a viable solution or should I use another MDS method? …
- 133