PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Kernel Perceptron on Noisy Data using Random Projections
Guillaume Stempfel and Liva Ralaivola
In: 18th International Conference on Algorithmic Learning Theory, October 1 - 4, 2007, Sendai, Japan.


In this paper, we address the issue of learning nonlinearly separable concepts with a kernel classifier in the situation where the data at hand are altered by a uniform classification noise. Our proposed approach relies on the combination of the technique of random or deterministic projections with a classification noise tolerant perceptron learning algorithm that assumes distributions defined over finite-dimensional spaces. Provided a sufficient separation margin characterizes the problem, this strategy makes it possible to envision the learning from a noisy distribution in any separable Hilbert space, regardless of its dimension; learning with any appropriate Mercer kernel is therefore possible. We prove that the required sample complexity and running time of our algorithm is polynomial in the classical PAC learning parameters. Numerical simulations on toy datasets and on data from the UCI repository support the validity of our approach.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:3562
Deposited By:Liva Ralaivola
Deposited On:11 February 2008