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Authors: M. Pérez-Ortiz ; P. A. Gutiérrez and C. Hervás-Martínez

Affiliation: University of Córdoba, Spain

ISBN: 978-989-758-054-3

Keyword(s): Manifold Learning, Ordinal Regression, Privileged Information, Kernel Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Support Vector Machines and Applications ; Theory and Methods

Abstract: Manifold learning covers those learning algorithms where high-dimensional data is assumed to lie on a lowdimensional manifold (usually nonlinear). Specific classification algorithms are able to preserve this manifold structure. On the other hand, ordinal regression covers those learning problems where the objective is to classify patterns into labels from a set of ordered categories. There have been very few works combining both ordinal regression and manifold learning. Additionally, privileged information refers to some special features which are available during classifier training, but not in the test phase. This paper contributes a new algorithm for combining ordinal regression and manifold learning, based on the idea of constructing a neighbourhood graph and obtaining the shortest path between all pairs of patterns. Moreover, we propose to exploit privileged information during graph construction, in order to obtain a better representation of the underlying manifold. The approach is tested with one synthetic experiment and 5 real ordinal datasets, showing a promising potential. (More)

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Paper citation in several formats:
Pérez-Ortiz, M.; A. Gutiérrez, P. and Hervás-Martínez, C. (2014). Incorporating Privileged Information to Improve Manifold Ordinal Regression.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 187-194. DOI: 10.5220/0005075801870194

@conference{ncta14,
author={M. Pérez{-}Ortiz. and P. A. Gutiérrez. and C. Hervás{-}Martínez.},
title={Incorporating Privileged Information to Improve Manifold Ordinal Regression},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={187-194},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005075801870194},
isbn={978-989-758-054-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Incorporating Privileged Information to Improve Manifold Ordinal Regression
SN - 978-989-758-054-3
AU - Pérez-Ortiz, M.
AU - A. Gutiérrez, P.
AU - Hervás-Martínez, C.
PY - 2014
SP - 187
EP - 194
DO - 10.5220/0005075801870194

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