Multiple kernel learning for spectral dimensionality reduction

Abstract

This work introduces a multiple kernel learning (MKL) approach for selecting and combining different spectralmethods of dimensionality reduction (DR).From a predefined set of kernels representing conventional spectralDRmethods, a generalized kernel is calculated by means of a linear combination of kernel matrices. Coefficients are estimated via a variable ranking aimed at quantifying how much each variable contributes to optimize a variance preservation criterion. All considered kernels are testedwithinakernelPCAframework.Theexperiments are carriedoutover well-known real and artificial data sets. The performance of compared DR approaches is quantified by a scaled version of the average agreement rate between K-ary neighborhoods. Proposed MKL approach exploits the representation ability of every single method to reach a better embedded data for both getting more intelligible visualization and preserving the structure of data.