Work

A Geometric Algorithm for Scalable Multiple Kernel Learning

Year: 2012

Type: preprint

Abstract: 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 novel 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 a uniform unweighted combination of kernels.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1206.5580

Authors: Moeller, John, Raman, Parasaran, Saha, Avishek, Venkatasubramanian, Suresh

Institutions: University of Utah, Yahoo (United States)

Language: English

Topic: Machine Learning and Algorithms

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Award: CAREER: Geometric Algorithms For Data Analysis In Spaces Of Distributions

OpenAlex ID: W2134373213

Cites: 24

Cited by: 4

Related to: 20