Learning using privileged information in prototype based models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Colleges, School and Institutes

External organisations

  • Centre for Cardiovascular Sciences; School of Clinical and Experimental Medicine; University of Birmingham; Edgbaston Birmingham UK

Abstract

In some pattern analysis problems, there exists expert knowledge, in addition to the original data involved in the classification process. Most of existing approaches ignore such auxiliary (privileged) knowledge. Recently a new learning paradigm - Learning Using Hidden Information - was introduced in the SVM+ framework. This approach is formulated for binary classification and, as typical for many kernel based methods, can scale unfavorably with the number of training examples. In this contribution we present a more direct novel methodology, based on a prototype metric learning model, for incorporation of valuable privileged knowledge. This is done by changing the global metric in the input space, based on distance relations revealed by the privileged information. Our method achieves competitive performance against the SVM+ formulations. We also present a successful application of our method to a large scale multi-class real world problem of galaxy morphology classification.

Details

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 2012
Event22nd International Conference on Artificial Neural Networks, ICANN 2012 - Lausanne, Switzerland
Duration: 11 Sep 201214 Sep 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7553 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference22nd International Conference on Artificial Neural Networks, ICANN 2012
CountrySwitzerland
CityLausanne
Period11/09/1214/09/12

Keywords

  • Generalized Matrix Learning Vector Quantization (GMLVQ), Information Theoretic Metric Learning (ITML), Learning Using Hidden Information (LUHI)