Unsupervised embedded feature learning for deep clustering with stacked sparse auto-encoder

作者：

Highlights：

• Propose a new deep clustering method by introducing sparse embedded learning.

• Learn an effective embedded representation in the hidden layer.

• Improved the local structure retention strategy by exploiting the sparse constraint.

• Present a joint optimization framework for feature learning and data clustering.

摘要

•Propose a new deep clustering method by introducing sparse embedded learning.•Learn an effective embedded representation in the hidden layer.•Improved the local structure retention strategy by exploiting the sparse constraint.•Present a joint optimization framework for feature learning and data clustering.

论文关键词：Machine learning,Deep clustering,Feature representation,Auto-encoder,Neural networks

论文评审过程：Received 16 January 2020, Revised 10 February 2021, Accepted 4 August 2021, Available online 16 August 2021, Version of Record 25 August 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115729