Publications
CONFERENCE (INTERNATIONAL) Learning Extreme Multi-label Tree-classifier via Nearest Neighbor Graph Partitioning
Yukihiro Tagami
The 26th International Conference on World Wide Web (Posters) (WWW 2017)
April 01, 2017
Web scale classification problems, such as Web page tagging and E-commerce product recommendation, are typically regarded as multi-label classification with an extremely large number of labels. In this paper, we propose GPT, which is a novel tree-based approach for extreme multi-label learning. GPT recursively splits a feature space with a hyperplane at each internal node, considering approximate $k$-nearest neighbor graph on the label space. We learn the linear binary classifiers using a simple optimization procedure. We conducted evaluations on several large-scale real-world data sets and compared our proposed method with recent state-of-the-art methods. Experimental results demonstrate the effectiveness of our proposed method.
Paper :
Learning Extreme Multi-label Tree-classifier via Nearest Neighbor Graph Partitioning
(external link)