---
title: "Multi-View Learning with Limited and Noisy Tagging"
authors:
  - "Yingming Li"
  - "Ming Yang"
  - "Zenglin Xu"
  - "Zhongfei Zhang"
doi: ""
url: "https://ijcai.org/proceedings/2016/246"
pdf: "https://ijcai.org/proceedings/2016/0246.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "1718-1723"
track: ""
keywords:
---

# Multi-View Learning with Limited and Noisy Tagging

Yingming Li, Ming Yang, Zenglin Xu, Zhongfei Zhang

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 1718-1723.

## Abstract

Multi-view tagging has become increasingly popular in the applications where data representations by multiple views exist. A robust multi-view tagging method must have the capability to meet the two challenging requirements: limited labeled training samples and noisy labeled training samples. In this paper, we investigate this challenging problem of learning with limited and noisy tagging and propose a discriminative model, called MSMC, that exploits both labeled and unlabeled data through a semi-parametric regularization and takes advantage of the multi-label space consistency into the optimization. While MSMC is a general method for learning with multi-view, limited, and noisy tagging, in the evaluations we focus on the specific application of noisy image tagging with limited labeled training samples on a benchmark dataset. Extensive evaluations in comparison with state-of-the-art literature demonstrate that MSMC outstands with a superior performance.