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Results tagged with pca
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user 40604
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.
1
vote
0
answers
865
views
Explained variance threshold for eigenfaces
So for example, if I want use n components that explain $95\%$ of variance, and I was considering the first 3 components then I can do this:
import numpy as np
from sklearn.decomposition import PCA
from … = PCA().fit(lfw_people.data)
explained_variance = np.cumsum(pca.explained_variance_ratio_)
n = sum(explained_variance <= 0.95)
This results in an n of 150. …
- 1,437
4
votes
Accepted
A guide to regularization strategies in regression
From The Elements of Statistical Learning, as suggested by goangit, section 3.6 is a one page discussion comparing selection and shrinkage methods which points to a paper by Frank and Friedman (1993) …
CommunityBot
- 1
5
votes
2
answers
665
views
A guide to regularization strategies in regression
I'm looking for some sort of guideline as when it is appropriate to use which forms of regularization and a comparison of the advantages / disadvantages of the various forms. So something that compare …
CommunityBot
- 1