Spectral clustering via ensemble deep autoencoder learning (SC-EDAE)

作者：

Highlights：

• We propose a robust and efficient deep clustering approach with no pre-training.

• We combine spectral clustering, deep embeddings and ensemble paradigm strengths.

• Our original clustering method inherits the low complexity of landmarks strategy.

• The effectiveness is shown through extensive experiments on real-world datasets.

摘要

•We propose a robust and efficient deep clustering approach with no pre-training.•We combine spectral clustering, deep embeddings and ensemble paradigm strengths.•Our original clustering method inherits the low complexity of landmarks strategy.•The effectiveness is shown through extensive experiments on real-world datasets.

论文关键词：Spectral clustering,Unsupervised ensemble learning,Autoencoder,

论文评审过程：Received 12 June 2019, Revised 24 June 2020, Accepted 29 June 2020, Available online 3 July 2020, Version of Record 14 July 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107522