时间：2022-10-01
本文（ Multi-view Representation Learning for Data Stream Clustering）原载Information Sciences，由四川大学王竹教授等科研人员创作，系四川大学市场经济法治研究所系列学术成果。后续会持续分享四川大学市场经济法治研究所系列学术成果，欢迎大家阅读。
Data stream clustering provides valuable insights into the evolving patterns of long
sequences of continuously generated data objects. Most existing clustering methods focus
on single-view data streams. In this paper, we propose a multi-view representation learn
ing (MVRL) method for multi-view clustering of data streams. We first introduce an inte
grated representation learning model to learn a fused sparse affinity matrix across multiple
views for spectral clustering. Motivated by the optimization procedure of the integrated
representation learning model, we propose three consecutive stages: collaborative repre
sentation, the construction of individual global affinity matrices using a mapping function,
and the calculation of a fused sparse affinity matrix using Euclidean projection. These
stages allow the effective capture of the global and local structures of high-dimensional
data objects. Moreover, each stage has a closed-form solution, which determines the upper
bound of the computational cost and memory consumption. We then employ the construc
tion residuals of the collaborative representation to adaptively update a dynamic set,
which is used to preserve the representative data objects. The dynamic set efficiently trans
fers previously learned useful knowledge to the arriving data objects. Extensive experi
mental results on multi-view data stream datasets demonstrate the effectiveness of the
proposed MVRL method.
Jie Chen, Shengxiang Yang, and Zhu Wang. Multi-view Representation Learning for Data Stream Clustering. Information Sciences 613 (2022) 731–746.（论文下载）
