Representation learning using deep random vector functional link networks for clustering

作者：

Highlights：

• Unsupervised RVFL (usRVFL) framework based on manifold regularization proposed to perform unsupervised representation learning.

• Embedded features allow usRVFL to perform unsupervised tasks such as clustering.

• Inspired by consensus clustering, unsupervised deep RVFL proposed.

• Evaluations on benchmark datasets have shown that usdRVFL achieves equivalent or superior clustering performance to state-of-the-art spectral clustering methods.

摘要

•Unsupervised RVFL (usRVFL) framework based on manifold regularization proposed to perform unsupervised representation learning.•Embedded features allow usRVFL to perform unsupervised tasks such as clustering.•Inspired by consensus clustering, unsupervised deep RVFL proposed.•Evaluations on benchmark datasets have shown that usdRVFL achieves equivalent or superior clustering performance to state-of-the-art spectral clustering methods.

论文关键词：Random vector functional link,Unsupervised learning,Consensus clustering,Manifold regularization

论文评审过程：Received 5 March 2021, Revised 12 February 2022, Accepted 24 April 2022, Available online 26 April 2022, Version of Record 2 May 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108744