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Incorporating Privileged Information to Improve Manifold Ordinal RegressionTopics: Learning Paradigms and Algorithms; Pattern Recognition; Support Vector Machines and Applications

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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.
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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.

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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