SIAM Journal on Matrix Analysis and Applications · 2022 · 12 citations · 40 references
ComputingComputer ScienceMathematicsBiomedical Signal ProcessingInformation TheoryRepresentation TheoryDigital Signal ProcessingDimensionality ReductionMatrix FactorizationSignal ProcessingBlind Signal SeparationBlind Source SeparationPolesSparse Signal RepresentationStrong UniquenessTensor DecompositionsTensor FactorizationUniqueness Conditions
We derive new uniqueness results for (Lᵣ,Lᵣ,1)-type block-term decompositions of third-order tensors by drawing connections to sparse component analysis. It is shown that our uniqueness results have a natural application in the context of the blind source separation problem, since they ensure uniqueness even among (Lᵣ,Lᵣ,1)-decompositions with incomparable rank profiles, allowing for stronger separation results for signals consisting of sums of exponentials in the presence of common poles among the source signals. As a byproduct, this line of ideas also suggests a new approach for computing (Lᵣ,Lᵣ,1)-decompositions, which proceeds by sequentially computing a canonical polyadic decomposition of the input tensor, followed by performing a sparse factorization on the third factor matrix.
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Proceedings of the 21st International Conference on Neural Information Processing Systems
Daphne Koller, Dale Schuurmans, Yoshua Bengio et al. · 2008 · 4.5K citations