A Peek at the Landscape of Dictionary Learning
11 Feb 2020, 15:00 — Genova
Speaker:
Karin Schnass — Universität Innsbruck
Karin Schnass — Universität Innsbruck
Abstract:
In this talk we will visit the landscape of dictionary learning via iterative thresholiding and K residual means. For a givengenerating dictionary we will have a look at the basin of attraction, the regions of contraction, and spurious attractive points. Time permitting we will also discuss heuristics how to use escape from spurious attractive points and jump directly into the basin of attraction.
In this talk we will visit the landscape of dictionary learning via iterative thresholiding and K residual means. For a givengenerating dictionary we will have a look at the basin of attraction, the regions of contraction, and spurious attractive points. Time permitting we will also discuss heuristics how to use escape from spurious attractive points and jump directly into the basin of attraction.
Bio:
Karin Schnass holds an MSc in Mathematics from the University of Vienna (AT), 2004, and a PhD in Computer, Communication and Information Sciences from EPFL (CH), 2009. Following a postdoc at RICAM Linz (AT) and two maternity leaves, she spent 2 years as Schroedinger fellow at the University of Sassari (IT). In 2015 she joined the University of Innsbruck first as assistant professer, since 2019 as associate professor, where she is heading the FWF-START-project ‘Optimisation Principles, Models & Algorithms for Dictionary Learning' (6 yrs, 1.2M€). She is an expert on (theoretical) dictionary learning.
Karin Schnass holds an MSc in Mathematics from the University of Vienna (AT), 2004, and a PhD in Computer, Communication and Information Sciences from EPFL (CH), 2009. Following a postdoc at RICAM Linz (AT) and two maternity leaves, she spent 2 years as Schroedinger fellow at the University of Sassari (IT). In 2015 she joined the University of Innsbruck first as assistant professer, since 2019 as associate professor, where she is heading the FWF-START-project ‘Optimisation Principles, Models & Algorithms for Dictionary Learning' (6 yrs, 1.2M€). She is an expert on (theoretical) dictionary learning.