John Moeller

Machine Learning Researcher

A Geometric Algorithm for Scalable Multiple Kernel Learning

2014-04 AISTATS Reykjavik, Iceland
Author(s): John Moeller, Parasaran Raman, Suresh Venkatasubramanian, and Avishek Saha

We present a geometric formulation of the Multiple Kernel Learning (MKL) problem. To do so, we reinterpret the problem of learning kernel weights as searching for a kernel that maximizes the minimum (kernel) distance between two convex polytopes. This interpretation combined with additional structural insights from our geometric formulation allows us to reduce the MKL problem to a simple optimization routine that yields provable convergence as well as quality guarantees. As a result our method scales efficiently to much larger data sets than most prior methods can handle. Empirical evaluation on eleven datasets shows that we are significantly faster and even compare favorably with an uniform unweighted combination of kernels.


I defended my PhD thesis in Computer Science at the University of Utah School of Computing, under Suresh Venkatasubramanian. My specialties are in machine learning and algorithms. I am no longer on the market! I'll be joining Kitware in Fall of 2016.