Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual ppca Daniel@0: Daniel@0: Daniel@0: Daniel@0:

ppca Daniel@0:

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Daniel@0: Purpose Daniel@0:

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

Daniel@0: Synopsis Daniel@0:

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

Daniel@0: Description Daniel@0:

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

Daniel@0: See Also Daniel@0:

Daniel@0: eigdec, pca

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

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