## Bayesian Machine Learning Algorithms for Discovering Relations with Latent Variable Models

### Tapani Raiko's Academy of Finland postdoctoral researcher's project 2010-2012

Adaptive Informatics Research Centre, Department of Information and
Computer Science, Aalto University School of Science and Technology

The project is about developing graphical models that can find new
types of latent sources from data. These sources describe
relationships between observations instead of describing the
observations directly, as in traditional methods. Graphical models are
a class of statistical machine learning methods that combine graphs as
a model structure with probability theory for inference on the
variables at the nodes of the graph. Latent variable models such as
independent component analysis, are graphical models that explain the
patterns in observed data by latent sources. In the proposed
methodology, the activity of a source can indicate for instance a
correlation between some of the observations.

Applications for the proposed methods are done in close collaboration
with domain experts. The collaboration includes robotics with the
ZenRobotics company, climate modelling using the NCEP/NCAR Reanalysis
data with Alexander Ilin, texture analysis in content based image
retrieval, speech recognition in noisy environments, analysis of
complex time series in collaboration with Xerox Research Centre
Europe, and collaborative filtering in social networks such as
Last.fm.

During the project, the research focus has drifted slightly towards deep learning.

Figure on top right: The model structure for

drifting linear dynamics. The continuous valued hidden state s(t) affects the linear dynamics of the observation sequence x(t).