Opponent: Professor Florence d'Alché-Buc
Custos: Professor Petri Myllymaki

Abstract:

The core aim of machine learning is to make a computer program learn
from the experience. Learning from data is usually defined as a task
of learning regularities or patterns in data in order to extract
useful information, or to learn the underlying concept. An important
subfield of machine learning is called multi-view learning where the
task is to learn from multiple data sets or views describing the same
underlying concept. A typical example of such scenario would be to
study a biological concept using several biological measurements like
gene expression, protein expression and metabolic profiles, or to
classify web pages based on their content and the contents of their
hyperlinks

In this thesis, novel problem formulations and methods for multi-view
learning are presented. The contributions include a linear data fusion
approach during exploratory data analysis, a new measure to evaluate
different kinds of representations for textual data, and an extension
of multi-view learning for novel scenarios where the correspondence of
samples in the different views or data sets is not known in advance.
In order to infer the one-to-one correspondence of samples between two
views, a novel concept of multi-view matching is proposed. The
matching algorithm is completely data-driven and is demonstrated in
several applications such as matching of metabolites between humans
and mice, and matching of sentences between documents in two
languages.