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EECS Seminar: When Dictionary Learning Meets Graph Signal Processing

April 24 @ 1:00 pm - 2:00 pm

When Dictionary Learning Meets Graph Signal Processing

Yael Yankelevsky
Dept. of Computer Science
Technion University

Abstract:
Dictionary Learning techniques aim to find sparse signal representations that capture prominent characteristics in the given data. For signals residing on non-Euclidean topologies, represented by weighted graphs, an additional challenge is incorporating the underlying geometric structure of the data domain into the learning process.
In this talk, we introduce an approach that aims to infer and preserve the local intrinsic geometry in both dimensions of the data. Combining ideas from spectral graph theory, manifold learning and sparse representations, our proposed algorithm simultaneously takes into account the underlying graph topology as well as the data manifold structure. The efficiency of this approach is demonstrated on a variety of applications, including sensor network data completion and enhancement, image structure inference, and challenging multi-label classification problems.
Bio:
Yael holds B.Sc. and M.Sc. degrees in Electrical Engineering from the Technion. She is currently a PhD student in the Computer Science Department at the Technion, working under the supervision of Prof. Michael Elad. Her research interests include signal and image processing, machine learning, sparse representations, inverse problems and graphical models.

Details

Date:
April 24
Time:
1:00 pm - 2:00 pm
Event Category:

Organizer

Amir Rosenfel

Venue

Lassonde 3033

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