Multi-label classification using hierarchical embedding
作者：
Highlights：
• Multi-label learning deals with the classification of data with multiple labels.
• Output space with many labels is tackle by modeling inter-label correlations.
• Use of parametrization and embedding have been the prime focus.
• A piecewise-linear embedding using maximum margin matrix factorization is proposed.
• Our experimental analysis manifests the superiority of our proposed method.
摘要
•Multi-label learning deals with the classification of data with multiple labels.•Output space with many labels is tackle by modeling inter-label correlations.•Use of parametrization and embedding have been the prime focus.•A piecewise-linear embedding using maximum margin matrix factorization is proposed.•Our experimental analysis manifests the superiority of our proposed method.
论文关键词：Multi-label learning,Matrix factorization,Label correlation
论文评审过程：Received 11 May 2017, Revised 25 August 2017, Accepted 9 September 2017, Available online 11 September 2017, Version of Record 13 September 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.09.020
