Ensemble of Independent Factor Analyzers with Application to
Natural Image Analysis
Neural Processing Letters, vol.14, no.1, pp. 49-60
In this paper, the ensemble of independent factor
analyzers (EIFA) is proposed.
This new statistical model
assumes that each data point is generated by
the sum of outputs of independently activated factor analyzers.
A maximum likelihood (ML) estimation algorithm for the parameters
is derived using a Monte Carlo EM algorithm with a Gibbs sampler.
The EIFA model is applied to natural image data.
With the progress of the learning,
the independent factor analyzers develop into feature detectors
that resemble complex cells in mammalian visual systems.
Although this result is similar to the previous one
obtained by independent subspace analysis,
we observe the emergence of complex cells from natural images
in a more general framework of models,
including overcomplete models allowing additive noise in the observables.