Joint Tensor Factorization and Outlying Slab Suppression With Applications

Xiao Fu, Kejun Huang, Wing Kin Ma, Nicholas D. Sidiropoulos, Rasmus Bro

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


We consider factoring low-rank tensors in the presence of outlying slabs. This problem is important in practice, because data collected in many real-world applications, such as speech, fluorescence, and some social network data, fit this paradigm. Prior work tackles this problem by iteratively selecting a fixed number of slabs and fitting, a procedure which may not converge. We formulate this problem from a group-sparsity promoting point of view, and propose an alternating optimization framework to handle the corresponding ℓp (0< p ≤ 1) minimization-based low-rank tensor factorization problem. The proposed algorithm features a similar per-iteration complexity as the plain trilinear alternating least squares (TALS) algorithm. Convergence of the proposed algorithm is also easy to analyze under the framework of alternating optimization and its variants. In addition, regularization and constraints can be easily incorporated to make use of a priori information on the latent loading factors. Simulations and real data experiments on blind speech separation, fluorescence data analysis, and social network mining are used to showcase the effectiveness of the proposed algorithm.

Original languageEnglish (US)
Article number7208891
Pages (from-to)6315-6328
Number of pages14
JournalIEEE Transactions on Signal Processing
Issue number23
StatePublished - Dec 1 2015

Bibliographical note

Funding Information:
The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Andre Almeida. The work of X. Fu, K. Huang, and N. D. Sidiropoulos was supported in part by NSF IIS-1247632.

Publisher Copyright:
© 2015 IEEE.


  • Canonical polyadic decomposition
  • group sparsity
  • iteratively reweighted
  • outliers
  • robustness
  • tensor decomposition


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