DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification

TL;DR

DiSMEC is a scalable distributed framework for extreme multi-label classification that avoids low-rank assumptions, efficiently learns classifiers for hundreds of thousands of labels, and improves accuracy over state-of-the-art methods.

Contribution

DiSMEC introduces a distributed, one-vs-rest linear classifier framework with explicit capacity control, handling large label spaces without low-rank assumptions.

Findings

Can learn classifiers for datasets with up to 670,000 labels within hours.

Achieves 10% better accuracy than SLEEC on some datasets.

Achieves 15% better accuracy than FastXML on some datasets.

Abstract

Extreme multi-label classification refers to supervised multi-label learning involving hundreds of thousands or even millions of labels. Datasets in extreme classification exhibit fit to power-law distribution, i.e. a large fraction of labels have very few positive instances in the data distribution. Most state-of-the-art approaches for extreme multi-label classification attempt to capture correlation among labels by embedding the label matrix to a low-dimensional linear sub-space. However, in the presence of power-law distributed extremely large and diverse label spaces, structural assumptions such as low rank can be easily violated. In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size. Unlike most state-of-the-art methods, DiSMEC does not make any low rank assumptions on the label matrix. Using double layer of parallelization, DiSMEC can learn classifiers for datasets consisting hundreds of thousands labels within few hours. The explicit capacity control mechanism filters out spurious parameters which keep the model compact in size, without losing prediction accuracy. We conduct extensive empirical evaluation on publicly available real-world datasets consisting upto 670,000 labels. We compare DiSMEC with recent state-of-the-art approaches, including - SLEEC which is a leading approach for learning sparse local embeddings, and FastXML which is a tree-based approach optimizing ranking based loss function. On some of the datasets, DiSMEC can significantly boost prediction accuracies - 10% better compared to SLECC and 15% better compared to FastXML, in absolute terms.