Bayesian latent variable models for learning dependencies between multiple data sources

Machine learning focuses on automated large-scale data analysis extracting useful information from data collections. The data are frequently high-dimensional and may correspond, for example, to images, text documents, or measurements of neural responses. In many applications data can be collected from multiple data sources, that is, views.

This thesis presents novel machine learning methods for analyzing multiple data sources, especially for understanding relationships between them. The analysis provides a comprehensive summary of the data generating process, which may be used for exploring the relationships and for predicting observations of one or more sources. The methods are based on two assumptions: each view provides complementary information of the data generating process, and each view is corrupted by noise. The methods aim to utilize all available information (views), accumulating partly overlapping information and reducing view-specific noise.