当前位置: X-MOL 学术Int. J. Mach. Learn. & Cyber. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
LSTM-assisted evolutionary self-expressive subspace clustering
International Journal of Machine Learning and Cybernetics ( IF 5.6 ) Pub Date : 2021-06-29 , DOI: 10.1007/s13042-021-01363-z
Di Xu , Mingyuan Bai , Tianhang Long , Junbin Gao

Massive volumes of high-dimensional data that evolve over time are continuously collected by contemporary information processing systems, which bring up the problem of organizing these data into clusters, i.e. achieving the purpose of dimensional reduction, and meanwhile learning their temporal evolution patterns. In this paper, a framework for evolutionary subspace clustering, referred to as LSTM–ESCM, is introduced, which aims at clustering a set of evolving high-dimensional data points that lie in a union of low-dimensional evolving subspaces. In order to obtain the parsimonious data representation at each time step, we propose to exploit the so-called self-expressive trait of the data at each time point. At the same time, LSTM networks are implemented to extract the inherited temporal patterns behind data in the overall time frame. An efficient algorithm has been proposed. Numerous experiments are carried out on real-world datasets to demonstrate the effectiveness of our proposed approach. The results show that the suggested algorithm dramatically outperforms other known similar approaches in terms of both run time and accuracy.



中文翻译:

LSTM 辅助的进化自我表达子空间聚类

当代信息处理系统不断收集大量随时间演化的高维数据,这就带来了将这些数据组织成簇的问题,即达到降维的目的,同时学习它们的时间演化模式。在本文中,介绍了一种进化子空间聚类框架,称为 LSTM-ESCM,其目的是对位于低维进化子空间联合中的一组进化高维数据点进行聚类。为了在每个时间点获得简约的数据表示,我们建议利用每个时间点数据的所谓自我表达特征。同时,实施 LSTM 网络以提取整个时间范围内数据背后的继承时间模式。已经提出了一种有效的算法。在真实世界的数据集上进行了大量实验,以证明我们提出的方法的有效性。结果表明,所建议的算法在运行时间和准确性方面都显着优于其他已知的类似方法。

更新日期:2021-06-29
down
wechat
bug