Speeding up Extreme Multi-Label Classifier by Approximate Nearest Neighbor Search

Yukihiro TAGAMI  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.11   pp.2784-2794
Publication Date: 2018/11/01
Publicized: 2018/08/06
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7096
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
extreme multi-label classification,  k-nearest neighbor graph,  approximate nearest neighbor search,  learning-to-rank,  

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Extreme multi-label classification methods have been widely used in Web-scale classification tasks such as Web page tagging and product recommendation. In this paper, we present a novel graph embedding method called “AnnexML”. At the training step, AnnexML constructs a k-nearest neighbor graph of label vectors and attempts to reproduce the graph structure in the embedding space. The prediction is efficiently performed by using an approximate nearest neighbor search method that efficiently explores the learned k-nearest neighbor graph in the embedding space. We conducted evaluations on several large-scale real-world data sets and compared our method with recent state-of-the-art methods. Experimental results show that our AnnexML can significantly improve prediction accuracy, especially on data sets that have a larger label space. In addition, AnnexML improves the trade-off between prediction time and accuracy. At the same level of accuracy, the prediction time of AnnexML was up to 58 times faster than that of SLEEC, a state-of-the-art embedding-based method.