LSTM-Assisted Evolutionary Self-Expressive Subspace Clustering
This addresses the challenge of organizing time-evolving high-dimensional data for information processing systems, but appears incremental as it combines existing concepts like self-expressive clustering and LSTM networks.
The paper tackles the problem of clustering evolving high-dimensional data into low-dimensional subspaces while learning temporal patterns, introducing the LSTM-ESCM framework. Results show it dramatically outperforms similar approaches in run time and accuracy on real-world datasets.
Massive volumes of high-dimensional data that evolves over time is continuously collected by contemporary information processing systems, which brings up the problem of organizing this data into clusters, i.e. achieve the purpose of dimensional deduction, and meanwhile learning its 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 an overall time frame. An efficient algorithm has been proposed based on MATLAB. Next, experiments are carried out on real-world datasets to demonstrate the effectiveness of our proposed approach. And the results show that the suggested algorithm dramatically outperforms other known similar approaches in terms of both run time and accuracy.