Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
On Convergence of an Iterative Factor Estimate Algorithm for the NFA Model
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures
Neural Computation
BYY harmony learning, independent state space, and generalized APT financial analyses
IEEE Transactions on Neural Networks
On Convergence of an Iterative Factor Estimate Algorithm for the NFA Model
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
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In this paper we comparatively study three MAP factor estimate approaches, i.e., iterative fixed posteriori approximation, gradient descent approach, and conjugate gradient algorithm, for the non-Gaussian factor analysis (NFA). With the so-called Gaussian approximation as initialization, the iterative fixed posteriori approximation is empirically found to be the best one among them.