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Publication - Mr Rafael Poyiadzi

    Label Propagation for Learning with Label Proportions

    Citation

    Poyiadzi, R, Santos-Rodriguez, R & Twomey, N, 2018, ‘Label Propagation for Learning with Label Proportions’. in: 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP 2018): Proceedings of a meeting held 17-20 September 2018, Aalborg, Denmark. Institute of Electrical and Electronics Engineers (IEEE), pp. 264-270

    Abstract

    Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags. This paradigm is particularly suitable in contexts where providing individual labels is expensive and label aggregates are more easily obtained. In the healthcare domain, it is a burden for a patient to keep a detailed diary of their daily routines, but often they will be amenable to provide higher level summaries of daily behavior. We present a novel and efficient graph-based algorithm that encourages local smoothness and exploits the global structure of the data, while preserving the 'mass' of each bag.

    Full details in the University publications repository