Robust deep k-means: An effective and simple method for data clustering
作者：
Highlights：
• A novel robust deep model is proposed to perform k-means hierarchically, thus the hierarchical semantics of data can be explored in a layerwise way. As a result, data samples from the same class are effectively gathered closer layer by layer.
• To solve the optimization problem of our model, the corresponding objective function is derived to a more trackable form and an alternative updating algorithm is presented to solve the optimization problem.
• Experiments over 12 benchmark data sets are conducted and show promising results, compared to both classical and state-of-the-art methods.
摘要
•A novel robust deep model is proposed to perform k-means hierarchically, thus the hierarchical semantics of data can be explored in a layerwise way. As a result, data samples from the same class are effectively gathered closer layer by layer.•To solve the optimization problem of our model, the corresponding objective function is derived to a more trackable form and an alternative updating algorithm is presented to solve the optimization problem.•Experiments over 12 benchmark data sets are conducted and show promising results, compared to both classical and state-of-the-art methods.
论文关键词：k-means algorithm,Robust clustering,Deep learning
论文评审过程：Received 13 October 2020, Revised 10 March 2021, Accepted 18 April 2021, Available online 28 April 2021, Version of Record 9 May 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.107996
