Semi-supervised Learning for Ordinal Kernel Discriminant Analysis
Autor
Pérez-Ortiz, María
Gutiérrez, P.A.
Carbonero-Ruz, M.
Hervás-Martínez, César
Editor
ElsevierFecha
2016Materia
Ordinal regressionDiscriminant analysis
Semi-supervised learning
Classication
Kernel learning
METS:
Mostrar el registro METSPREMIS:
Mostrar el registro PREMISMetadatos
Mostrar el registro completo del ítemResumen
Ordinal classication considers those classication problems where the labels of
the variable to predict follow a given order. Naturally, labelled data is scarce
or di_cult to obtain in this type of problems because, in many cases, ordinal
labels are given by an user or expert (e.g. in recommendation systems). Firstly,
this paper develops a new strategy for ordinal classi_cation where both labelled
and unlabelled data are used in the model construction step (a scheme which
is referred to as semi-supervised learning). More specically, the ordinal version
of kernel discriminant learning is extended for this setting considering the
neighbourhood information of unlabelled data, which is proposed to be computed
in the feature space induced by the kernel function. Secondly, a new
method for semi-supervised kernel learning is devised in the context of ordinal
classi_cation, which is combined with our developed classi_cation strategy to
optimise the kernel parameters. The experiments conducted compare 6 different
approaches for semi-supervised learning in the context of ordinal classication
in a battery of 30 datasets, showing 1) the good synergy of the ordinal version
of discriminant analysis and the use of unlabelled data and 2) the advantage of
computing distances in the feature space induced by the kernel function.