6533b835fe1ef96bd129e9e9

RESEARCH PRODUCT

Learning with the kernel signal to noise ratio

Gustau Camps-vallsLuis Gómez-chova

subject

Kernel methodSignal-to-noise ratioKernel embedding of distributionsPolynomial kernelbusiness.industryVariable kernel density estimationKernel (statistics)Radial basis function kernelPattern recognitionArtificial intelligencebusinessKernel principal component analysisMathematics

description

This paper presents the application of the kernel signal to noise ratio (KSNR) in the context of feature extraction to general machine learning and signal processing domains. The proposed approach maximizes the signal variance while minimizes the estimated noise variance in a reproducing kernel Hilbert space (RKHS). The KSNR can be used in any kernel method to deal with correlated (possibly non-Gaussian) noise. We illustrate the method in nonlinear regression examples, dependence estimation and causal inference, nonlinear channel equalization, and nonlinear feature extraction from high-dimensional satellite images. Results show that the proposed KSNR yields more fitted solutions and extracts more noise-free features when confronted with standard approaches.

yearjournalcountryeditionlanguage
2012-09-012012 IEEE International Workshop on Machine Learning for Signal Processing
https://doi.org/10.1109/mlsp.2012.6349715