An inexact splitting method for the subspace segmentation from incomplete and noisy observations

Renli Liang, Yanqin Bai*, Hai Xiang Lin

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
28 Downloads (Pure)

Abstract

Subspace segmentation is a fundamental issue in computer vision and machine learning, which segments a collection of high-dimensional data points into their respective low-dimensional subspaces. In this paper, we first propose a model for segmenting the data points from incomplete and noisy observations. Then, we develop an inexact splitting method for solving the resulted model. Moreover, we prove the global convergence of the proposed method. Finally, the inexact splitting method is implemented on the clustering problems in synthetic and benchmark data, respectively. Numerical results demonstrate that the proposed method is computationally efficient, robust as well as more accurate compared with the state-of-the-art algorithms.

Original languageEnglish
Pages (from-to)411–429
Number of pages19
JournalJournal of Global Optimization
Volume73 (2019)
DOIs
Publication statusPublished - 2018

Bibliographical note

Accepted author manuscript

Keywords

  • Inexact augmented Lagrange multiplier method
  • Low rank representation
  • Subspace segmentation

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