FoCM 2014 conference

Workshop C3 - Learning Theory

December 19, 15:30 ~ 16:00 - Room B23

Democratic Learning: Learning to Represent Data for Everybody

Guillermo Sapiro

Duke University, USA   -

In this talk I will describe a simple framework for learning data transforms that are computationally free and help diverse classification and clustering algorithms. When incorporated into standard techniques such as subspace clustering, random forests, and hashing codes, we obtain one to two orders of magnitude improvement at virtually no cost. I will present both the underlying concepts and applications ranging from scene recognition to image classification to 3D object analysis.

Joint work with Qiang Qiu (Duke University) and Alex Bronstein (Tel Aviv University).

View abstract PDF