Bayesian Multi-label Learning with Sparse Features and Labels, and Label Co-occurrences

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    We present a probabilistic, fully Bayesian framework for multi-label learning. Our framework is based on the idea of learning a joint low-rank embedding of the label matrix and the label co-occurrence matrix. The proposed framework has the following appealing aspects: (1) It leverages the sparsity in the label matrix and the feature matrix, which results in very efficient inference, especially for sparse datasets, commonly encountered in multi-label learning problems, and (2) By effectively utilizing the label co-occurrence information, the model yields improved prediction accuracies, especially in the case where the amount of training data is low and/or the label matrix has a significant fraction of missing labels. Our framework enjoys full local conjugacy and admits a simple inference procedure via a scalable Gibbs sampler. We report experimental results on a number of benchmark datasets, on which it outperforms several state-of-the-art multi-label learning models.
    Original languageEnglish
    Title of host publication2018 Twenty-First International Conference on Artificial Intelligence and Statistics, AISTATS 2018
    Subtitle of host publication9-11 April 2018, Lanzarote, Canary Islands, Proceedings
    EditorsAmos Storkey, Fernando Perez-Cruz
    Place of PublicationUSA
    PublisherProceedings of Machine Learning Research (PMLR)
    Number of pages9
    Publication statusPublished - 2018
    EventInternational Conference on Artificial Intelligence and Statistics 2018 - Playa Blanca, Canary Islands, Spain
    Duration: 9 Apr 201811 Apr 2018
    Conference number: 21st (Proceedings)

    Publication series

    NameProceedings of Machine Learning Research
    PublisherProceedings of Machine Learning Research (PMLR)
    ISSN (Print)1938-7228


    ConferenceInternational Conference on Artificial Intelligence and Statistics 2018
    Abbreviated titleAISTATS 2018
    CityPlaya Blanca, Canary Islands
    Internet address

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