Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual pca Daniel@0: Daniel@0: Daniel@0: Daniel@0:

pca Daniel@0:

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Daniel@0: Purpose Daniel@0:

Daniel@0: Principal Components Analysis Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

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

Daniel@0: Description Daniel@0:

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

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

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

Daniel@0: See Also Daniel@0:

Daniel@0: eigdec, gtminit, ppca

Daniel@0: Pages: Daniel@0: Index Daniel@0:

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