Feature vector dimension reduction (PCA)
조회 수: 8 (최근 30일)
이전 댓글 표시
Hello,
How can reduce a feature vector of dimension K*N to a feature vectore of dimension K*M with M<N (image classification task)?
I read about PCA but I am not understanding how can I use it to get the K*M vector.
Appreciate your help!
댓글 수: 4
J. Alex Lee
2021년 6월 9일
I'm not sure what is returned by pca(), but presumably coeff is KxN (the rotated coefficents)? Then is your question how to decide M? Is score a vector 1xN?
채택된 답변
the cyclist
2021년 6월 9일
I have written an answer to this question that explains in detail how to use MATLAB's pca function, including how to do dimensional reduction. I suggest that you read that question, answer, comments from other users, and my responses. I expect this will answer your question.
댓글 수: 4
the cyclist
2021년 6월 11일
Use the coeff matrix from the PCA you did previously, to transform the 1xN vector in the original space into a 1xN vector in the PC space, then use the first M columns. That 1xM vector is the feature-reduced vector in the new space.
추가 답변 (0개)
참고 항목
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!