Autoencoder-like semi-NMF multiple clustering

作者：Yao, Shihong; Hu, Chuli^*; Wang, Tao; Cui, Xinyou

来源：Information Sciences, 2021, 572: 331-342.

DOI：10.1016/j.ins.2021.04.080

• Autoencoder
• Semi-NMF
• Multiple clustering

Clustering is performed to partition samples into disjoint groups for facilitating the discovery of hidden patterns in the data. Many real-world applications involve various clustering methods, most of which only produce a single clustering. As a response to this issue, multiple clustering that aims to generate diverse and high-quality clustering, has emerged recently. This study proposes a novel autoencoder-like semi-nonnegative matrix factorization (NMF) multiple clustering (ASNMFMC) model that generates multiple non-redundant, high-quality clustering. The nonnegative property of the semi-NMF is utilized by the algorithm to enforce non-redundancy. Extensive experimental results demonstrate that the ASNMFMC is superior to the existing multiple clustering methods and can explore diverse high-quality clustering.