Incorporating privileged information to improve manifold ordinal regression

Research areas:

• Methodology - Machine learning

Year:

2014

Type of Publication:

In Proceedings

Keywords:

Manifold Learning, Ordinal Regression, Privileged Information, Kernel Learning

Authors:

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

Book title:

6th International Conference on Neural Computation Theory and Applications (NCTA2014)

Pages:

187-194

Organization:

Roma (Italy)

Month:

22th-24th October

ISBN:

978-989-758-054-3

BibTex:

@conference{1620141, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Manifold learning covers those learning algorithms where high-dimensional data is assumed to lie on a low-dimensional 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 specialfeatures 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 aneighbourhood graph and obtaining the shortest path between all pairs of patterns. Moreover, we proposeto exploit privileged information during graph construction, in order to obtain a better representation of theunderlying manifold. The approach is tested with one synthetic experiment and 5 real ordinal datasets, showinga promising potential.", booktitle = "6th International Conference on Neural Computation Theory and Applications (NCTA2014)", isbn = "978-989-758-054-3", keywords = "Manifold Learning, Ordinal Regression, Privileged Information, Kernel Learning", month = "22th-24th October", organization = "Roma (Italy)", pages = "187-194", publisher = "SCITEPRESS", title = "{I}ncorporating privileged information to improve manifold ordinal regression", url = "http://www.ijcci.org/Program/2014/Program_Friday.htm", year = "2014", }

Abstract:

Manifold learning covers those learning algorithms where high-dimensional data is assumed to lie on a low-dimensional 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 specialfeatures 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 aneighbourhood graph and obtaining the shortest path between all pairs of patterns. Moreover, we proposeto exploit privileged information during graph construction, in order to obtain a better representation of theunderlying manifold. The approach is tested with one synthetic experiment and 5 real ordinal datasets, showinga promising potential.