Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
Sequential EM learning for subspace analysis
Pattern Recognition Letters
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We have recently developed an extension of a Principal Component Analysis Artificial Neural Network which we have linked to the statistical technique of Factor Analysis. The learning rule can be shown to be optimal for data sets corrupted by Gaussian noise. We now derive from a new cost function a novel learning rule which is optimal for a standard data set. We compare both rules on a data set composed of 10 faces in a mixture of poses. The first learning rule performs best on the face data. Our conclusion is that the first rule which is optimal for Gaussian noise is more generally useful but that specific rules may be optimal for finding the independent factors underlying specific data sets dependent on the noise in the data set.