Kernel Conjugate Gradient

Nathan Ratliff and J. Andrew (Drew) Bagnell
tech. report CMU-RI-TR-05-30, Robotics Institute, Carnegie Mellon University, July, 2005


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Abstract
We propose a novel variant of conjugate gradient based on the Reproducing Kernel Hilbert Space (RKHS) inner product. An analysis of the algorithm suggests it enjoys better performance properties than standard iterative methods when applied to learning kernel machines. Experimental results for both classification and regression bear out the theoretical implications. We further address the dominant cost of the algorithm by reducing the complexity of RKHS function evaluations and inner products through the use of space-partitioning tree data-structures.

Keywords
kernel methods, functional gradient, reproducing kernel hilbert spaces, conjugate gradient, kernel logistic regression, regularized least squares, gaussian processes, kd-trees

Notes
Associated Center(s) / Consortia: Center for the Foundations of Robotics
Associated Lab(s) / Group(s): Planning and Autonomy Lab
Associated Project(s): Learning Locomotion
Number of pages: 8

Text Reference
Nathan Ratliff and J. Andrew (Drew) Bagnell, "Kernel Conjugate Gradient," tech. report CMU-RI-TR-05-30, Robotics Institute, Carnegie Mellon University, July, 2005

BibTeX Reference
@techreport{Ratliff_2005_5051,
   author = "Nathan Ratliff and J. Andrew (Drew) Bagnell",
   title = "Kernel Conjugate Gradient",
   booktitle = "",
   institution = "Robotics Institute",
   month = "July",
   year = "2005",
   number= "CMU-RI-TR-05-30",
   address= "Pittsburgh, PA",
}