EE Seminar: Non-smooth manifold optimization with applications to machine learning and pattern recognition

~~(The talk will be given in English)

Speaker:   Prof. Michael Bronstein
                        University of Lugano, Switzerland / Perceptual Computing, Intel, Israel RAS, Moscow, Russia

Sunday, April 3rd, 2016
14:00 - 15:00
Room 011, Kitot Bldg., Faculty of Engineering

Non-smooth manifold optimization with applications to machine learning and pattern recognition

Abstract
Numerous problems in machine learning are formulated as optimization with manifold constraints, i.e., where the variables are restricted to a smooth submanifold of the search space. For example, optimization on the Grassman manifold comes up in multi-view clustering and matrix completion; Stiefel manifolds arise in eigenvalue-, assignment-, and Procrustes problems, compressed sensing, shape correspondence, manifold learning, sensor localization, structural biology, and structure from motion recovery; manifolds of fixed-rank matrices appear in maxcut problems and sparse principal component analysis; and oblique manifolds are encountered in problems such as joint diagonalization and blind source separation.
In this talk, I will present an ADMM-like method allowing to handle non-smooth manifold-constrained optimization. Our method is generic and not limited to a specific manifold, is very simple to implement, and does not require parameter tuning. I will show examples of applications from the domains of physics, computer graphics, and machine learning.