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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Efficient and Scalable Nonlinear Multiple Kernel Aggregation Using the Choquet Integral
verfasst von : Lequn Hu, Derek T. Anderson, Timothy C. Havens, James M. Keller
Erschienen in: Information Processing and Management of Uncertainty in Knowledge-Based Systems
Verlag: Springer International Publishing
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Previously, we investigated the definition and applicability of the
fuzzy integral
(FI) for nonlinear
multiple kernel
(MK) aggregation in pattern recognition. Kernel theory provides an elegant way to map multi-source heterogeneous data into a combined homogeneous (implicit) space in which aggregation can be carried out. The focus of our initial work was the Choquet FI, a per-matrix sorting based on the quality of a base learner and learning was restricted to the Sugeno
λ
-
fuzzy measure
(FM). Herein, we investigate what representations of FMs and FIs are valid and ideal for nonlinear MK aggregation. We also discuss the benefit of our approach over the linear convex sum MK formulation in machine learning. Furthermore, we study the Möbius transform and k-additive integral for scalable
MK learning
(MKL). Last, we discuss an extension to our
genetic algorithm
(GA) based MKL algorithm, called FIGA, with respect to a combination of multiple
light weight
FMs and FIs.