wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual ppca wolffd@0: wolffd@0: wolffd@0: wolffd@0:

ppca wolffd@0:

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wolffd@0: Purpose wolffd@0:

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

wolffd@0: Synopsis wolffd@0:

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wolffd@0: [var, U, lambda] = pca(x, ppca_dim)
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wolffd@0: Description wolffd@0:

wolffd@0: wolffd@0: [var, U, lambda] = ppca(x, ppca_dim) computes the principal component wolffd@0: subspace U of dimension ppca_dim using a centred wolffd@0: covariance matrix x. The variable var contains wolffd@0: the off-subspace variance (which is assumed to be spherical), while the wolffd@0: vector lambda contains the variances of each of the principal wolffd@0: components. This is computed using the eigenvalue and eigenvector wolffd@0: decomposition of x. wolffd@0: wolffd@0:

wolffd@0: See Also wolffd@0:

wolffd@0: eigdec, pca

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

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