Principal Component Analysis - linear or nonlinear relationships between variables?

Nola Aguilar

Nola Aguilar

Answered question

2022-11-05

Principal Component Analysis - linear or nonlinear relationships between variables?

Answer & Explanation

Liehm1mm

Liehm1mm

Beginner2022-11-06Added 13 answers

PCA is inherently linear being based on linear algebra. Of course the principles behind it may be extended to algorithms for nonlinear data, but they differ markedly from what we're used to with PCA.
PCA is used for dimensionality reduction. This could be in the concrete sense of finding 2 D planes in 3 D data or the more abstract of given a problem over 20 parameters, try to trim it down to say 2 or 3 combinations thereof in order to be managable computationally.
E.g. given a D dimensional problem space, can we apply an orthogonal transformation so that the solution only depends on d

Do you have a similar question?

Recalculate according to your conditions!

New Questions in Inferential Statistics

Ask your question.
Get an expert answer.

Let our experts help you. Answer in as fast as 15 minutes.

Didn't find what you were looking for?