Feature vector dimension reduction (PCA)

4 次查看（过去 30 天）
Andrea Daou
Andrea Daou 2021-6-9
评论： Andrea Daou 2021-6-11
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!
  4 个评论
显示 2更早的评论
Andrea Daou
Andrea Daou 2021-6-9
编辑：Andrea Daou 2021-6-9
I read about [coeff, score] = pca(features) but for example if I have a dimesion equal to 1340*5435 and I want to pass to 1340*M, is new_features = score(:,1:M) a good solution ?
This solution has a limitation: M cannot take a value > 1340
Thank you in advance,
J. Alex Lee
J. Alex Lee 2021-6-9
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?

请先登录，再进行评论。

请先登录，再回答此问题。

采纳的回答

the cyclist
the cyclist 2021-6-9
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.
  4 个评论
显示 2更早的评论
the cyclist
the cyclist 2021-6-11
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.

请先登录，再进行评论。

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Dimensionality Reduction and Feature Extraction 的更多信息

标签

Community Treasure Hunt

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

Start Hunting!

Translated by