principle component analysis (PCA)

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Ramin
Ramin 2020-5-22
评论： Rik 2020-5-22
hi everyone. there is a matrix of 5100*720 dimension. each class includes 2550 sample. each row represents a sample of the classes. the pca is applied on each class. the problem is the output matrix is a 720*720. the question is in the output matrix which dimension represents the class samples? or which dimension must be assumed as the classes?
thanks
coeffs = pca(P300);

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Rik
Rik 2020-5-22
Read the documentation:
coeff = pca(X) returns the principal component coefficients, also known as loadings, for the n-by-p data matrix X. Rows of X correspond to observations and columns correspond to variables. The coefficient matrix is p-by-p. Each column of coeff contains coefficients for one principal component, and the columns are in descending order of component variance. By default, pca centers the data and uses the singular value decomposition (SVD) algorithm.
  2 个评论
Ramin
Ramin 2020-5-22
yes. you are right. but now the rows are the classes and columns are the principle components?
Rik
Rik 2020-5-22
I don't know what you would mean by classes in this latter context, but yes, each column is a component.

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