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

Xiaodong Zheng, Shanfeng Zhu, Junning Gao, Hiroshi Mamitsuka

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Machine Learning. 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.

Xiaodong Zheng, Shanfeng Zhu, Junning Gao, and Hiroshi Mamitsuka. 2015. Instance-Wise Weighted Nonnegative Matrix Factorization for Aggregating Partitions with Locally Reliable Clusters. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 4025–4030. https://ijcai.org/proceedings/2015/574
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