LSTM-Assisted Evolutionary Self-Expressive Subspace Clustering

@article{Xu2021LSTMAssistedES,
  title={LSTM-Assisted Evolutionary Self-Expressive Subspace Clustering},
  author={Di Xu and Tianhang Long and Junbin Gao},
  journal={Int. J. Mach. Learn. Cybern.},
  year={2021},
  volume={12},
  pages={2777-2793}
}
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… 

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