Learning using privileged information in prototype based models

Shereen Fouad*, Peter Tino, Somak Raychaudhury, Petra Schneider

*Corresponding author for this work

    Research output: Chapter in Book/Published conference outputConference publication


    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.

    Original languageEnglish
    Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings
    Number of pages8
    EditionPART 2
    ISBN (Print)9783642332654
    Publication statusPublished - 2012
    Event22nd International Conference on Artificial Neural Networks, ICANN 2012 - Lausanne, Switzerland
    Duration: 11 Sept 201214 Sept 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)0302-9743
    ISSN (Electronic)1611-3349


    Conference22nd International Conference on Artificial Neural Networks, ICANN 2012


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


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