||Instance-wise weighted nonnegative matrix factorization for aggregating partitions with locally reliable clusters
Zheng, Xiaodong ,
Zhu, Shanfeng ,
Gao, JunningMamitsuka, Hiroshi
Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI 15)
4097 , 2015-07-25 , AAAI Press
IJCAI-15: Buenos Aires, Argentina, 25–31 July 2015
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.