principle component analysis (PCA)

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);

Answer 1

[Rik]

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.

[Ramin]

yes. you are right. but now the rows are the classes and columns are the principle components?

[Rik]

I don't know what you would mean by classes in this latter context, but yes, each column is a component.