Semi-Supervised Learning For Ordinal Kernel Discriminant Analysis

Áreas de investigación:

• Methodology - Machine learning

Año:

2016

Tipo de publicación:

Artículo

Palabras clave:

Ordinal regression, Discriminant analysis, Semi-supervised learning, Classification, Kernel learning

Autores:

• Pérez-Ortiz, María
• Gutiérrez, Pedro Antonio
• Carbonero-Ruz, Mariano
• Hervás-Martínez, César

Journal:

Neural Networks

Volumen:

84

Páginas:

57-66

Mes:

December

ISSN:

0893-6080

BibTex:

@article{PerezOrtiz2016SemiSupiNeunet, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and Mariano Carbonero-Ruz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult 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 classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, 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 classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification 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.", awards = "JCR(2016): 5.287 Position: 10/133 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2016): 5.287 Position: 10/133 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.neunet.2016.08.004", issn = "0893-6080", journal = "Neural Networks", keywords = "Ordinal regression, Discriminant analysis, Semi-supervised learning, Classification, Kernel learning", month = "December", note = "JCR(2016): 5.287 Position: 10/133 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "57--66", title = "{S}emi-{S}upervised {L}earning {F}or {O}rdinal {K}ernel {D}iscriminant {A}nalysis", url = "http://dx.doi.org/10.1016/j.neunet.2016.08.004", volume = "84", year = "2016", }

Nota:

JCR(2016): 5.287 Position: 10/133 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Abstract:

Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult 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 classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, 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 classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification 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.

Comentarios:

JCR(2016): 5.287 Position: 10/133 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE