Nonnegative matrix factorization for clustering ensemble based on dark knowledge

作者：Ye, Wenting; Wang, Hongjun^*; Yan, Shan; Li, Tianrui; Yang, Yan

来源：Knowledge-Based Systems, 2019, 163: 624-631.

DOI：10.1016/j.knosys.2018.09.021

• Cluster ensemble
• Nonnegative matrix factorization
• Dark knowledge

Traditional cluster ensemble (CE) methods use labels produced by base learning algorithms to obtain an ensemble result. These base learning algorithms can also obtain other information, such as parameter, covariance, or probability data, which is called dark knowledge. In this paper, we propose a method for integrating dark knowledge, which is usually ignored, into the ensemble learning process. This provides more information about the base clustering. We apply nonnegative matrix factorization (NMF) to the clustering ensemble model based on dark knowledge. First, different base clustering results are obtained by using various clustering configurations, before dark knowledge of every base clustering algorithm is extracted. NMF is then applied to the dark knowledge to obtain integrated results. Experimental results show that the method outperforms other clustering ensemble techniques.

• 出版日期2019-1-1
• 单位西南交通大学