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This thesis consists of eight publications and an introductory part
with literature survey. The aim of the thesis is to develop a
computationally efficient algorithm for a nonlinear extension of the
linear factor analysis model.
- The main result of this thesis is the development of an
algorithm which is able to learn nonlinear factor analysis models
with a relatively high number of factors. The computational
complexity scales quadratically with respect to the dimension of
factor space which is already efficient enough for many interesting
applications, but it is also possible to use the model as an
elementary module in larger models which scale linearly in the total
size of the representation.
- Bayesian probability theory and decision theory provide the
theoretical framework for learning, inference and decision making.
In this thesis, a computationally feasible approximation of the
exact Bayesian learning, termed ensemble learning, is used. The
manner in which ensemble learning can be applied to nonlinear factor
analysis is presented.
- It is shown how the nonlinear model can be combined with other
extensions of factor analysis which relax the Gaussianity assumption
of the factors and include a model for the dynamics of the factors.
Section 2 of the introductory part summarises the
publications of the thesis, with the contributions of the author
explained. Section 3 outlines the theoretical
framework of Bayesian probability theory and decision theory.
Practical methods and approximations together with their connection to
information theory are discussed in section 4.
Section 5 introduces the basic static Gaussian linear
factor analysis model and its non-Gaussian, nonlinear and dynamic
extensions. Publication V serves as a detailed
account on the nonlinear factor analysis method developed in this thesis but
section 6 gives a brief summary. Biological
relevance and further lines of research are discussed in
section 7.
Next: PUBLICATIONS OF THE THESIS
Up: INTRODUCTION
Previous: INTRODUCTION
Harri Valpola
2000-10-31