LSTM-Assisted Evolutionary Self-Expressive Subspace Clustering

TL;DR

This paper introduces LSTM-ESCM, a novel framework combining LSTM networks and self-expressive subspace clustering to effectively organize evolving high-dimensional data into low-dimensional subspaces, capturing temporal patterns.

Contribution

The paper proposes a new evolutionary subspace clustering framework that integrates LSTM networks with self-expressive data representations for dynamic high-dimensional data.

Findings

Outperforms existing methods in accuracy

Demonstrates faster run times

Effectively captures temporal evolution patterns

Abstract

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.