All positive examples are alike; each negative example is negative in its own way. During interactive multimedia information retrieval, the number of training samples fed-back by the user is usually small; furthermore, they are not representative for the true distributions-especially the negative examples. Adding to the difficulties is the nonlinearity in real-world distributions. Existing solutions fail to address these problems in a principled way. This paper proposes biased discriminant analysis and transforms specifically designed to address the asymmetry between the positive and negative examples, and to trade off generalization for robustness under a small training sample. The kernel version, namely "BiasMap ", is derived to facilitate nonlinear biased discrimination. Extensive experiments are carried out for performance evaluation as compared to the state-of-the-art methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Small sample learning during multimedia retrieval using BiasMap


    Contributors:


    Publication date :

    2001-01-01


    Size :

    779894 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Small Sample Learning during Multimedia Retrieval Using BiasMap

    Zhou, X. S. / Huang, T. S. / IEEE | British Library Conference Proceedings | 2001


    MIRACLE: Multimedia Information Retrieval by Analyzing Content and Learning from Examples

    Lei, Z. / Ganapathy, S. K. / Safranek, R. J. | British Library Conference Proceedings | 1998


    EUROPAN SAMPLE RETRIEVAL SYSTEM

    Espinosa, M.W. / Butler, H.S. / Lee, Y.C. et al. | British Library Conference Proceedings | 2012


    Europan Sample Retrieval System

    Espinosa, Mirella / Butler, Hazel / Lee, Yong Chul | AIAA | 2012


    Adaptive Pattern Discovery for Interactive Multimedia Retrieval

    Wu, Y. / Zhang, A. / IEEE | British Library Conference Proceedings | 2003