---
title: "Incomplete Multi-Modal Visual Data Grouping"
authors:
  - "Handong Zhao"
  - "Hongfu Liu"
  - "Yun Fu"
doi: ""
url: "https://ijcai.org/proceedings/2016/341"
pdf: "https://ijcai.org/proceedings/2016/0341.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "2392-2397"
track: ""
keywords:
---

# Incomplete Multi-Modal Visual Data Grouping

Handong Zhao, Hongfu Liu, Yun Fu

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 2392-2397.

## Abstract

Nowadays multi-modal visual data are much easier to access as the technology develops. Nevertheless, there is an underlying problem hidden behind the emerging multi-modality techniques: What if one/more modal data fail? Motivated by this question, we propose an unsupervised method which well handles the incomplete multi-modal data by transforming the original and incomplete data to a new and complete representation in a latent space. Different from the existing efforts that simply project data from each modality into a common subspace, a novel graph Laplacian term with a good probabilistic interpretation is proposed to couple the incomplete multi-modal samples. In such a way, a compact global structure over the entire heterogeneous data is well preserved, leading to a strong grouping discriminability. As a non-trivial contribution, we provide the optimization solution to the proposed model. In experiments, we extensively test our method and competitors on one synthetic data, two RGB-D video datasets and two image datasets. The superior results validate the benefits of the proposed method, especially when multi-modal data suffer from large incompleteness.