NIPS Workshop on Kernel Learning: Automatic Selection of Optimal Kernels, Whistler 2008

Second Order Optimization of Kernel Parameters

presenter: Francis R. Bach, INRIA - SIERRA project-team
published: Dec. 20, 2008, recorded: December 2008, views: 357

Slides

Slides
0:00	Second order optimization of kernel parameters
1:05	Introduction
1:43	Previous work (1)
3:32	Previous work (2)
3:35	Objective function (1)
4:05	Objective function (2)
4:53	Objective function (3)
6:01	Optimization
7:00	Second order
7:27	Search direction
9:09	Complexity
10:00	Experiments (1)
11:31	Experiments (2)
12:15	Experiments (3)
13:36	Conclusion
16:00	- Questions

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Description

We investigate the use of second order optimization approaches for solving the multiple kernel learning (MKL) problem. We show that the hessian of the MKL can be computed eﬃciently and this information can be used to compute a better descent direction than the gradient (used in the state-of-the-art SimpleMKL algorithm). We then empirically show that our new approaches outperforms SimpleMKL in terms of computational eﬃciency.