---
title: "Instance-Wise Weighted Nonnegative Matrix Factorization for Aggregating Partitions with Locally Reliable Clusters"
authors:
  - "Xiaodong Zheng"
  - "Shanfeng Zhu"
  - "Junning Gao"
  - "Hiroshi Mamitsuka"
doi: ""
url: "https://ijcai.org/proceedings/2015/574"
pdf: "https://ijcai.org/proceedings/2015/0574.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "4025-4030"
track: "Special Track on Machine Learning"
keywords:
---

# Instance-Wise Weighted Nonnegative Matrix Factorization for Aggregating Partitions with Locally Reliable Clusters

Xiaodong Zheng, Shanfeng Zhu, Junning Gao, Hiroshi Mamitsuka

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 4025-4030.

## Abstract

We address an ensemble clustering problem, where reliable clusters are locally embedded in given multiple partitions. We propose a new nonnegative matrix factorization (NMF)-based method, in which locally reliable clusters are explicitly considered by using instance-wise weights over clusters. Our method factorizes the input cluster assignment matrix into two matrices H and W, which are optimized by iteratively 1) updating H and W while keeping the weight matrix constant and 2) updating the weight matrix while keeping H and W constant, alternatively. The weights in the second step were updated by solving a convex problem, which makes our algorithm significantly faster than existing NMF-based ensemble clustering methods. We empirically proved that our method outperformed a lot of cutting-edge ensemble clustering methods by using a variety of datasets.