New variables after computing Principal Components Analysis

조회 수: 1 (최근 30일)
NCA . 2021년 7월 5일
댓글: the cyclist . 2021년 7월 5일
After computing principal components analysis, what will represent my new variables to be used to build my model , is it the scores?
Thank You

채택된 답변

the cyclist
the cyclist 2021년 7월 5일
Yes, the output score is the observed values of the variables in the principal coordinate axes.
I wrote a detailed answer to this question that is a tutorial on how to use MATLAB's pca() function. You might find it helpful.
  댓글 수: 2
NCA 2021년 7월 5일
Thanks a lot for taking the time out to respond and answer my question, I really appreciate. Am I right to only use the first few columns of my scores as shown by 'explained' ( example 80% explained variance) or do I need to use score multiplied by coeff (score*coeff) to build my model?
Thank You
the cyclist
the cyclist 2021년 7월 5일
You want to use score, not score*coeff.
score is equivalent to X*coeff; it's the original observations transformed to the PC coordinates.

댓글을 달려면 로그인하십시오.

추가 답변 (1개)

Image Analyst
Image Analyst 2021년 7월 5일
Attached is a demo where I get the PCs of an RGB true color image, plus another demo.


Help CenterFile Exchange에서 Dimensionality Reduction and Feature Extraction에 대해 자세히 알아보기

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by