Collaboration based Multi-Label Learning

TL;DR

This paper introduces a novel multi-label learning method that explicitly models label correlations through collaboration, using sparse reconstruction to improve prediction accuracy over existing approaches.

Contribution

It proposes a new approach that learns label correlations via sparse reconstruction and integrates them into model training for better multi-label prediction.

Findings

Outperforms state-of-the-art methods in experiments

Explicitly models label collaboration during training

Uses sparse reconstruction to learn label correlations

Abstract

It is well-known that exploiting label correlations is crucially important to multi-label learning. Most of the existing approaches take label correlations as prior knowledge, which may not correctly characterize the real relationships among labels. Besides, label correlations are normally used to regularize the hypothesis space, while the final predictions are not explicitly correlated. In this paper, we suggest that for each individual label, the final prediction involves the collaboration between its own prediction and the predictions of other labels. Based on this assumption, we first propose a novel method to learn the label correlations via sparse reconstruction in the label space. Then, by seamlessly integrating the learned label correlations into model training, we propose a novel multi-label learning approach that aims to explicitly account for the correlated predictions of labels while training the desired model simultaneously. Extensive experimental results show that our approach outperforms the state-of-the-art counterparts.