Robust Orthonormal Subspace Learning (ROSL) for efficient low-rank recovery

Xianbiao Shu, Fatih Porikli, Narendra Ahuja

Research output: Chapter in Book/Report/Conference proceedingChapter

Original languageEnglish (US)
Title of host publicationHandbook of Robust Low-Rank and Sparse Matrix Decomposition
Subtitle of host publicationApplications in Image and Video Processing
PublisherCRC Press
Pages12.1-12.14
ISBN (Electronic)9781498724630
ISBN (Print)9781498724623
StatePublished - Jul 6 2016

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)
  • Engineering(all)

Cite this

Shu, X., Porikli, F., & Ahuja, N. (2016). Robust Orthonormal Subspace Learning (ROSL) for efficient low-rank recovery. In Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing (pp. 12.1-12.14). CRC Press.