Spectral rotation for deep one-step clustering

作者：

Highlights：

• Similarity matrix is obtained from the low-dimensional feature space of original data where both the influence of noise and the issue of high-dimensional data are considered.

• Optimized K-means clustering rotates original result of K-means clustering to search optimized clustering hyperplane which partition data points into clusters.

• Each of four parts (similarity matrix learning, spectral representation learning, optimized K-means clustering, and transformation matrix learning) is iteratively updated until convergence criteria is met.

摘要

•Similarity matrix is obtained from the low-dimensional feature space of original data where both the influence of noise and the issue of high-dimensional data are considered.•Optimized K-means clustering rotates original result of K-means clustering to search optimized clustering hyperplane which partition data points into clusters.•Each of four parts (similarity matrix learning, spectral representation learning, optimized K-means clustering, and transformation matrix learning) is iteratively updated until convergence criteria is met.

论文关键词：Similarity matrix learning,Spectral clustering,One-step clustering,Alternating direction method of multipliers

论文评审过程：Received 1 June 2019, Revised 3 October 2019, Accepted 15 December 2019, Available online 24 December 2019, Version of Record 5 June 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.107175