Joint low-rank approximation for extracting non-Gaussian subspaces

Motoaki Kawanabe, Fabian J. Theis

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


In this article, we consider high-dimensional data which contains a low-dimensional non-Gaussian structure contaminated with Gaussian noise. Motivated by the joint diagonalization algorithms, we propose a linear dimension reduction procedure called joint low-dimensional approximation (JLA) to identify the non-Gaussian subspace. The method uses matrices whose non-zero eigen spaces coincide with the non-Gaussian subspace. We also prove its global consistency, that is the true mapping to the non-Gaussian subspace is achieved by maximizing the contrast function defined by such matrices. As examples, we will present two implementations of JLA, one with the fourth-order cumulant tensors and the other with Hessian of the characteristic functions. A numerical study demonstrates validity of our method. In particular, the second algorithm works more robustly and efficiently in most cases.

Original languageEnglish
Pages (from-to)1890-1903
Number of pages14
JournalSignal Processing
Issue number8
StatePublished - Aug 2007
Externally publishedYes


  • Characteristic function
  • Fourth-order cumulant tensor
  • Joint low-rank approximation
  • Linear dimension reduction
  • Non-Gaussian subspace


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