## Factor analysis using delta-rule wake-sleep learning (1997)

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Venue: | Neural Computation |

Citations: | 24 - 3 self |

### BibTeX

@TECHREPORT{Neal97factoranalysis,

author = {Radford M. Neal and Peter Dayan},

title = {Factor analysis using delta-rule wake-sleep learning},

institution = {Neural Computation},

year = {1997}

}

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### Abstract

We describe a linear network that models correlations between real-valued visible variables using one or more real-valued hidden variables — a factor analysis model. This model can be seen as a linear version of the “Helmholtz machine”, and its parameters can be learned using the “wake-sleep ” method, in which learning of the primary “generative” model is assisted by a “recognition ” model, whose role is to fill in the values of hidden variables based on the values of visible variables. The generative and recognition models are jointly learned in “wake ” and “sleep ” phases, using just the delta rule. This learning procedure is comparable in simplicity to Oja’s version of Hebbian learning, which produces a somewhat different representation of correlations in terms of principal components. We argue that the simplicity of wake-sleep learning makes factor analysis a plausible alternative to Hebbian learning as a model of activity-dependent cortical plasticity. 1