会議発表論文 Instance-wise weighted nonnegative matrix factorization for aggregating partitions with locally reliable clusters

Zheng, Xiaodong  ,  Zhu, Shanfeng  ,  Gao, Junning  ,  Mamitsuka, Hiroshi

内容記述
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.
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http://repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/218488/1/IJCAI+2015_4091.pdf

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