Extreme Multi-label Classification from Aggregated Labels

• URL: http://arxiv.org/abs/2004.00198v1
• Date: Wed, 1 Apr 2020 02:13:09 GMT
• Title: Extreme Multi-label Classification from Aggregated Labels
• Authors: Yanyao Shen, Hsiang-fu Yu, Sujay Sanghavi, Inderjit Dhillon
• Abstract summary: Extreme multi-label classification (XMC) is the problem of finding the relevant labels for an input from a very large universe of possible labels. We develop a new and scalable algorithm to impute individual-sample labels from the group labels. This can be paired with any existing XMC method to solve the aggregated label problem.
• Score: 27.330826185375415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extreme multi-label classification (XMC) is the problem of finding the relevant labels for an input, from a very large universe of possible labels. We consider XMC in the setting where labels are available only for groups of samples - but not for individual ones. Current XMC approaches are not built for such multi-instance multi-label (MIML) training data, and MIML approaches do not scale to XMC sizes. We develop a new and scalable algorithm to impute individual-sample labels from the group labels; this can be paired with any existing XMC method to solve the aggregated label problem. We characterize the statistical properties of our algorithm under mild assumptions, and provide a new end-to-end framework for MIML as an extension. Experiments on both aggregated label XMC and MIML tasks show the advantages over existing approaches.

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