Topological analysis in information spaces
Event details
| Date | 24.11.2016 |
| Hour | 10:15 › 11:30 |
| Speaker | Hubert Wagner (IST Vienna) |
| Location |
MA 31
|
| Category | Conferences - Seminars |
Understanding high dimensional data remains a challenging problem.
Computational topology, in an effort dubbed Topological Data Analysis
(TDA), promises to simplify, characterize and compare such
data. However, TDA focuses on Euclidean spaces, while many types of
high-dimensional data naturally live in non-Euclidean ones. Spaces
derived from text, speech, image... data are best characterized by
non-metric dissimilarities, many of which are inspired by
information-theoretical concepts. Such spaces will be called
information spaces.
I will present the theoretical foundations of topological analysis in
information spaces. First, intuition behind basic computational
topology methods is given. Then, various dissimilarity measures are
defined along with information theoretical and geometric
interpretation. Finally, I will show that the framework of TDA can be
extended to information spaces and discuss the implications.
No previous knowledge about (computational) topology or information
theory is required. This is joint work with Herbert Edelsbrunner and
Ziga Virk. We look for interesting, practical applications for the
above!
Computational topology, in an effort dubbed Topological Data Analysis
(TDA), promises to simplify, characterize and compare such
data. However, TDA focuses on Euclidean spaces, while many types of
high-dimensional data naturally live in non-Euclidean ones. Spaces
derived from text, speech, image... data are best characterized by
non-metric dissimilarities, many of which are inspired by
information-theoretical concepts. Such spaces will be called
information spaces.
I will present the theoretical foundations of topological analysis in
information spaces. First, intuition behind basic computational
topology methods is given. Then, various dissimilarity measures are
defined along with information theoretical and geometric
interpretation. Finally, I will show that the framework of TDA can be
extended to information spaces and discuss the implications.
No previous knowledge about (computational) topology or information
theory is required. This is joint work with Herbert Edelsbrunner and
Ziga Virk. We look for interesting, practical applications for the
above!
Practical information
- Informed public
- Free
Organizer
- Kathryn Hess