On the evaluation of relevance learning by a multi-layer perceptron

Kenji Suzuki*, Shuji Hashimoto

*Corresponding author for this work

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

    1 Citation (Scopus)


    In this paper, we introduce a novel method of Relevance Learning by a multi-layer perceptron. The relevance learning is regarded as learning from the relationship among two or more outputs of the network. The learning network architecture is based on a simple multi-layer perceptron with a modified back-propagation learning algorithm. Unlike the conventional multi-layer perceptron that learns from a set of an input feature vector and the target output, the proposed network can obtain a nonlinear mapping between a set of two or more vector inputs and the desired relevance. For instance, the desired relevance represents the dissimilarity among given objects. We will show the performance of the proposed network with some experiments with four artificially generated data set. We then discuss the theoretical and mathematical background underlying the network learning with some related works. We evaluate the obtained arrangement of objects in comparison with the result of principle component analysis (PCA) and multi-dimensional scaling method (MDS). This work also contributes to the measurement of human subjective evaluation for multidimensional perceptual scaling. Some experimental results on the low-dimensional representation of color hue data set and emotional facial images will be presented.

    Original languageEnglish
    Title of host publicationProceedings of the International Joint Conference on Neural Networks
    Number of pages6
    Publication statusPublished - 2005
    EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC
    Duration: 2005 Jul 312005 Aug 4


    OtherInternational Joint Conference on Neural Networks, IJCNN 2005
    CityMontreal, QC

    ASJC Scopus subject areas

    • Software


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