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Talks & Seminars
Title: More Generality in Efficient Multiple Kernel Learning
Prof. Manik Varma, Researcher at MSRI and Adjunct professor at IIT Delhi
Date & Time: July 3, 2009 14:30
Venue: SIC 305, 03rd floor, ā€˜Cā€™ Block, Kanwal Rekhi Bldg.
Abstract:
Recent advances in Multiple Kernel Learning (MKL) have positioned it as an attractive tool for tackling many supervised learning tasks. The development of efficient gradient descent based optimization schemes has made it possible to tackle large scale problems. Simultaneously, MKL based algorithms have achieved very good results on challenging real world applications. Yet, despite their successes, MKL approaches are limited in that they focus on learning a linear combination of given base kernels. In this talk, we observe that existing MKL formulations can be extended to learn general kernel combinations subject to general regularization. This can be achieved while retaining all the efficiency of existing large scale optimization algorithms. To highlight the advantages of generalized kernel learning, we tackle feature selection problems on benchmark vision and UCI databases. It is demonstrated that the proposed formulation can lead to better results not only as compared to traditional MKL but also as compared to state-of-the-art wrapper and filter methods for feature selection.
Speaker Profile:
Manik received a bachelor's degree in Physics from St. Stephen's College, University of Delhi in 1997 and another one in Computation from the University of Oxford in 2000 on a Rhodes Scholarship. He then stayed on at Oxford on a University Scholarship and obtained a DPhil in Engineering in 2004. Before joining Microsoft Research India he was a Post-Doctoral Fellow at MSRI Berkeley. His research interests lie in the areas of machine learning and computer vision.
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