Machine Learning

Deep Extreme Multi-label Learning

Tagged: ,

This topic contains 0 replies, has 1 voice, and was last updated by  arXiv 1 month ago.


  • arXiv
    5 pts

    Deep Extreme Multi-label Learning

    Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves $2^L$ possible label sets when the label dimension $L$ is very large, e.g., in millions for Wikipedia labels. This paper is motivated to better explore the label space by building and modeling an explicit label graph. In the meanwhile, deep learning has been widely studied and used in various classification problems including multi-label classification, however it has not been sufficiently studied in this extreme but practical case, where the label space can be as large as in millions. In this paper, we propose a practical deep embedding method for extreme multi-label classification. Our method harvests the ideas of non-linear embedding and modeling label space with graph priors at the same time. Extensive experiments on public datasets for XML show that our method performs competitively against state-of-the-art result.

    Deep Extreme Multi-label Learning
    by Wenjie Zhang, Junchi Yan, Xiangfeng Wang, Hongyuan Zha
    https://arxiv.org/pdf/1704.03718v3.pdf

You must be logged in to reply to this topic.