## The doctoral dissertations of the former Helsinki University of Technology (TKK) and Aalto University Schools of Technology (CHEM, ELEC, ENG, SCI) published in electronic format are available in the electronic publications archive of Aalto University - Aaltodoc. | |

Dissertation for the degree of Doctor of Science in Technology to be presented with due
permission of the Department of Computer Science and Engineering for public examination
and debate in Auditorium T1 at Helsinki University of Technology (Espoo, Finland)
on the 1^{st} of December, 2006, at 12 o'clock noon.

Overview in PDF format (ISBN 951-22-8510-X) [1305 KB]

Dissertation is also available in print (ISBN 951-22-8509-6)

Statistical data analysis is becoming more and more important when growing amounts of data are collected in various fields of life. Automated learning algorithms provide a way to discover relevant concepts and representations that can be further used in analysis and decision making.

Graphical models are an important subclass of statistical machine learning that have clear semantics and a sound theoretical foundation. A graphical model is a graph whose nodes represent random variables and edges define the dependency structure between them. Bayesian inference solves the probability distribution over unknown variables given the data. Graphical models are modular, that is, complex systems can be built by combining simple parts. Applying graphical models within the limits used in the 1980s is straightforward, but relaxing the strict assumptions is a challenging and an active field of research.

This thesis introduces, studies, and improves extensions of graphical models that can be roughly divided into two categories. The first category involves nonlinear models inspired by neural networks. Variational Bayesian learning is used to counter overfitting and computational complexity. A framework where efficient update rules are derived automatically for a model structure given by the user, is introduced. Compared to similar existing systems, it provides new functionality such as nonlinearities and variance modelling. Variational Bayesian methods are applied to reconstructing corrupted data and to controlling a dynamic system. A new algorithm is developed for efficient and reliable inference in nonlinear state-space models.

The second category involves relational models. This means that observations may have distinctive internal structure and they may be linked to each other. A novel method called logical hidden Markov model is introduced for analysing sequences of logical atoms, and applied to classifying protein secondary structures. Algorithms for inference, parameter estimation, and structural learning are given. Also, the first graphical model for analysing nonlinear dependencies in relational data, is introduced in the thesis.

This thesis consists of an overview and of the following 9 publications:

- T. Raiko, H. Valpola, M. Harva, and J. Karhunen. Building Blocks for Variational Bayesian Learning of Latent Variable Models. Helsinki University of Technology, Publications in Computer and Information Science, Report E4, April, 2006. Journal of Machine Learning Research, accepted for publication conditioned on minor revisions.
- T. Raiko, H. Valpola, T. Östman, and J. Karhunen. Missing Values in Hierarchical Nonlinear Factor Analysis. In the Proceedings of the International Conference on Artificial Neural Networks and Neural Information Processing (ICANN/ICONIP 2003), pp. 185-189, Istanbul, Turkey, June 26-29, 2003.
- T. Raiko. Partially Observed Values. In the Proceedings of the International Joint Conference on Neural Networks (IJCNN 2004), pp. 2825-2830, Budapest, Hungary, July 25-29, 2004.
- T. Raiko and M. Tornio. Learning Nonlinear State-Space Models for Control. In the Proceedings of the International Joint Conference on Neural Networks (IJCNN 2005), pp. 815-820, Montreal, Canada, July 31 - August 4, 2005.
- T. Raiko, M. Tornio, A. Honkela, and J. Karhunen. State Inference in Variational Bayesian Nonlinear State-Space Models. In the Proceedings of the 6th International Conference on Independent Component Analysis and Blind Source Separation (ICA 2006), pp. 222-229, Charleston, South Carolina, USA, March 5-8, 2006.
- T. Raiko. Nonlinear Relational Markov Networks with an Application to the Game of Go. In the Proceedings of the International Conference on Artificial Neural Networks (ICANN 2005), pp. 989-996, Warsaw, Poland, September 11-15, 2005.
- K. Kersting, L. De Raedt, and T. Raiko. Logical Hidden Markov Models. In the Journal of Artificial Intelligence Research, Volume 25, pp. 425-456, April, 2006.
- K. Kersting, T. Raiko, S. Kramer, and L. De Raedt. Towards Discovering Structural Signatures of Protein Folds based on Logical Hidden Markov Models. In the Proceedings of the Pacific Symposium on Biocomputing (PSB 2003), pp. 192-203, Kauai, Hawaii, January 3-7, 2003.
- K. Kersting and T. Raiko. 'Say EM' for Selecting Probabilistic Models for Logical Sequences. In the Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence (UAI 2005), pp. 300-307, Edinburgh, Scotland, July 26-29, 2005.

**Keywords:**
machine learning, graphical models, probabilistic reasoning, nonlinear
models, variational methods, state-space models, hidden Markov models,
inductive logic programming, first-order logic

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© 2006 Helsinki University of Technology

Last update 2011-05-26