Deep Tensor Spectral Clustering Network Via Ensemble of Multiple Affinity Tensors

Hongmin Cai, Yu Hu, Fei Qi, Bin Hu, Yiu-ming Cheung*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


Tensor spectral clustering (TSC) is an emerging approach that explores multi- wise similarities to boost learning. However, two key challenges have yet to be well addressed in the existing TSC methods: (1) The construction and storage of high-order affinity tensors to encode the multi- wise similarities are memory-intensive and hampers their applicability, and (2) they mostly employ a two-stage approach that integrates multiple affinity tensors of different orders to learn a consensus tensor spectral embedding, thus often leading to a suboptimal clustering result. To this end, this paper proposes a tensor spectral clustering network (TSC-Net) to achieve one-stage learning of a consensus tensor spectral embedding, while reducing the memory cost. TSC-Net employs a deep neural network that learns to map the input samples to the consensus tensor spectral embedding, guided by a TSC objective with multiple affinity tensors. It uses stochastic optimization to calculate a small part of the affinity tensors, thereby avoiding loading the whole affinity tensors for computation, thus significantly reducing the memory cost. Through using an ensemble of multiple affinity tensors, the TSC can dramatically improve clustering performance. Empirical studies on benchmark datasets demonstrate that TSC-Net outperforms the recent baseline methods.
Original languageEnglish
Pages (from-to)5080-5091
Number of pages12
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number7
Early online date5 Feb 2024
Publication statusPublished - Jul 2024

Scopus Subject Areas

  • Software
  • Artificial Intelligence
  • Applied Mathematics
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics

User-Defined Keywords

  • Clustering
  • clustering ensemble
  • deep neural networks
  • tensor spectral clustering


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