Uniqueness of tensor decompositions with applications to polynomial identifiability

Aditya Bhaskara, Moses Charikar, Aravindan Vijayaraghavan

Research output: Contribution to journalConference articlepeer-review

32 Scopus citations


We give a robust version of the celebrated result of Kruskal on the uniqueness of tensor decompositions: given a tensor whose decomposition satisfies a robust form of Kruskal's rank condition, we prove that it is possible to approximately recover the decomposition if the tensor is known up to a sufficiently small (inverse polynomial) error. Kruskal's theorem has found many applications in proving the identifiability of parameters for various latent variable models and mixture models such as Hidden Markov models, topic models etc. Our robust version immediately implies identifiability using only polynomially many samples in many of these settings - an essential first step towards efficient learning algorithms. Our methods also apply to the "overcomplete" case, which has proved challenging in many applications. Given the importance of Kruskal's theorem in the tensor literature, we expect that our robust version will have several applications beyond the settings we explore in this work.

Original languageEnglish (US)
Pages (from-to)742-778
Number of pages37
JournalJournal of Machine Learning Research
StatePublished - 2014
Event27th Conference on Learning Theory, COLT 2014 - Barcelona, Spain
Duration: Jun 13 2014Jun 15 2014


  • Kruskal uniqueness theorem
  • Latent variable models
  • Tensor decomposition

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence


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