---
title: "Multi-Task Multi-View Clustering for Non-Negative Data"
authors:
  - "Xianchao Zhang"
  - "Xiaotong Zhang"
  - "Han Liu"
doi: ""
url: "https://ijcai.org/proceedings/2015/569"
pdf: "https://ijcai.org/proceedings/2015/0569.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "4062-4067"
track: "Special Track on Machine Learning"
keywords:
---

# Multi-Task Multi-View Clustering for Non-Negative Data

Xianchao Zhang, Xiaotong Zhang, Han Liu

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 4062-4067.

## Abstract

Multi-task clustering and multi-view clustering have severally found wide applications and received much attention in recent years. Nevertheless, there are many clustering problems that involve both multi-task clustering and multi-view clustering, i.e., the tasks are closely related and each task can be analyzed from multiple views. In this paper, for non-negative data (e.g., documents), we introduce a multi-task multi-view clustering (MTMVC) framework which integrates within-view-task clustering, multi-view relationship learning and multi-task relationship learning. We then propose a specific algorithm to optimize the MTMVC framework. Experimental results show the superiority of the proposed algorithm over either multi-task clustering algorithms or multi-view clustering algorithms for multi-task clustering of multi-view data.