---
title: "Semi-Supervised Multi-Label Learning with Incomplete Labels"
authors:
  - "Feipeng Zhao"
  - "Yuhong Guo"
doi: ""
url: "https://ijcai.org/proceedings/2015/570"
pdf: "https://ijcai.org/proceedings/2015/0570.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "4069-4074"
track: "Special Track on Machine Learning"
keywords:
---

# Semi-Supervised Multi-Label Learning with Incomplete Labels

Feipeng Zhao, Yuhong Guo

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 4069-4074.

## Abstract

The problem of incomplete labels is frequently encountered in many application domains where the training labels are obtained via crowd-sourcing. The label incompleteness significantly increases the difficulty of acquiring accurate multi-label prediction models. In this paper, we propose a novel semi-supervised multi-label method that integrates low-rank label matrix recovery into the manifold regularized vector-valued prediction framework to address multi-label learning with incomplete labels. The proposed method is formulated as a convex but non-smooth joint optimization problem over the latent label matrix and the prediction model parameters. We then develop a fast proximal gradient descent with continuation algorithm to solve it for a global optimal solution. The efficacy of the proposed approach is demonstrated on multiple multi-label datasets, comparing to related methods that handle incomplete labels.