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- W4372260520 abstract "It is well-known that the Tucker decomposition of a multi-dimensional tensor is not unique, because its factors are subject to rotation ambiguities similar to matrix factorization models. Inspired by the recent success in the identifiability of nonnegative matrix factorization, the goal of this work is to achieve similar results for nonnegative Tucker decomposition (NTD). We propose to add a matrix volume regularization as the identifiability criterion, and show that NTD is indeed identifiable if all of the Tucker factors satisfy the sufficiently scattered condition. We then derive an algorithm to solve the modified formulation of NTD that minimizes the generalized Kullback-Leibler divergence of the approximation plus the proposed matrix volume regularization. Numerical experiments show the effectiveness of the proposed method." @default.
- W4372260520 created "2023-05-07" @default.
- W4372260520 creator A5036053193 @default.
- W4372260520 creator A5057849844 @default.
- W4372260520 date "2023-06-04" @default.
- W4372260520 modified "2023-10-06" @default.
- W4372260520 title "Volume-Regularized Nonnegative Tucker Decomposition with Identifiability Guarantees" @default.
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- W4372260520 doi "https://doi.org/10.1109/icassp49357.2023.10096076" @default.
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