Image projection ridge regression for subspace clustering

Chong Peng, Zhao Kang, Fei Xu, Yongyong Chen, Qiang Cheng

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Subspace clustering methods have been widely studied recently. When the inputs are two-dimensional (2-D) data, existing subspace clustering methods usually convert them into vectors, which severely damages inherent structures and relationships from original data. In this letter, we propose a novel subspace clustering method for 2-D data. It directly uses 2-D data as inputs such that the learning of representations benefits from inherent structures and relationships of the data. It simultaneously seeks image projection and representation coefficients such that they mutually enhance each other and lead to powerful data representations. An efficient algorithm is developed to solve the proposed objective function with provable decreasing and convergence property. Extensive experimental results verify the effectiveness of the new method.

Original languageEnglish
Article number7918514
Pages (from-to)991-995
Number of pages5
JournalIEEE Signal Processing Letters
Issue number7
StatePublished - Jul 2017

Bibliographical note

Publisher Copyright:
© 2017 IEEE.


  • 2-diemnsional data
  • Spatial information
  • Subspace clustering
  • Unsupervised learning

ASJC Scopus subject areas

  • Signal Processing
  • Applied Mathematics
  • Electrical and Electronic Engineering


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