wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual pca wolffd@0: wolffd@0: wolffd@0: wolffd@0:

pca wolffd@0:

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wolffd@0: Purpose wolffd@0:

wolffd@0: Principal Components Analysis wolffd@0: wolffd@0:

wolffd@0: Synopsis wolffd@0:

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wolffd@0: PCcoeff = pca(data)
wolffd@0: PCcoeff = pca(data, N)
wolffd@0: [PCcoeff, PCvec] = pca(data)
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wolffd@0: Description wolffd@0:

wolffd@0: wolffd@0: PCcoeff = pca(data) computes the eigenvalues of the covariance wolffd@0: matrix of the dataset data and returns them as PCcoeff. These wolffd@0: coefficients give the variance of data along the corresponding wolffd@0: principal components. wolffd@0: wolffd@0:

PCcoeff = pca(data, N) returns the largest N eigenvalues. wolffd@0: wolffd@0:

[PCcoeff, PCvec] = pca(data) returns the principal components as wolffd@0: well as the coefficients. This is considerably more computationally wolffd@0: demanding than just computing the eigenvalues. wolffd@0: wolffd@0:

wolffd@0: See Also wolffd@0:

wolffd@0: eigdec, gtminit, ppca

wolffd@0: Pages: wolffd@0: Index wolffd@0:

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