Scalable Label Distribution Learning for Multi-Label Classification

TL;DR

This paper introduces SLDL, a scalable multi-label classification method that models label correlations as asymmetric distributions in a low-dimensional space, reducing computational complexity while maintaining high accuracy.

Contribution

The paper proposes a novel label distribution learning approach that handles asymmetric label correlations and scales efficiently to large label sets.

Findings

SLDL achieves competitive classification performance.

SLDL significantly reduces computational complexity.

The method effectively models asymmetric label correlations.

Abstract

Multi-label classification (MLC) refers to the problem of tagging a given instance with a set of relevant labels. Most existing MLC methods are based on the assumption that the correlation of two labels in each label pair is symmetric, which is violated in many real-world scenarios. Moreover, most existing methods design learning processes associated with the number of labels, which makes their computational complexity a bottleneck when scaling up to large-scale output space. To tackle these issues, we propose a novel method named Scalable Label Distribution Learning (SLDL) for multi-label classification which can describe different labels as distributions in a latent space, where the label correlation is asymmetric and the dimension is independent of the number of labels. Specifically, SLDL first converts labels into continuous distributions within a low-dimensional latent space and leverages the asymmetric metric to establish the correlation between different labels. Then, it learns the mapping from the feature space to the latent space, resulting in the computational complexity is no longer related to the number of labels. Finally, SLDL leverages a nearest-neighbor-based strategy to decode the latent representations and obtain the final predictions. Extensive experiments illustrate that SLDL achieves very competitive classification performances with little computational consumption.