Kernel two-dimensional ridge regression for subspace clustering

Chong Peng, Qian Zhang, Zhao Kang, Chenglizhao Chen, Qiang Cheng

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


Subspace clustering methods have been extensively studied in recent years. For 2-dimensional (2D) data, existing subspace clustering methods usually convert 2D examples to vectors, which severely damages inherent structural information and relationships of the original data. In this paper, we propose a novel subspace clustering method, named KTRR, for 2D data. The KTRR provides us with a way to learn the most representative 2D features from 2D data in learning data representation. In particular, the KTRR performs 2D feature learning and low-dimensional representation construction simultaneously, which renders the two tasks to mutually enhance each other. 2D kernel is introduced to the KTRR, which renders the KTRR to have enhanced capability of capturing nonlinear relationships from data. An efficient algorithm is developed for its optimization with provable decreasing and convergent property in objective value. Extensive experimental results confirm the effectiveness and efficiency of our method.

Original languageEnglish
Article number107749
JournalPattern Recognition
StatePublished - May 2021

Bibliographical note

Publisher Copyright:
© 2020


  • 2-dimensional
  • Kernel
  • Ridge regression
  • Subspace clustering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


Dive into the research topics of 'Kernel two-dimensional ridge regression for subspace clustering'. Together they form a unique fingerprint.

Cite this