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Block-Term Tensor Decomposition: Model Selection and Computation
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DOI：10.1109/JSTSP.2021.3051488.png)
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摘要
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The so-called block-term decomposition (BTD) tensor model has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are composed of blocks of rank higher than one, a scenario encountered in numerous and diverse applications. Its uniqueness and approximation have thus been thoroughly studied. Nevertheless, the challenging problem of estimating the BTD model structure, namely the number of block terms and their individual ranks, has only recently started to attract significant attention. In this paper, a novel method of BTD model selection and computation is proposed, based on the idea of imposing column sparsity jointly on the factors and in a hierarchical manner and estimating the ranks as the numbers of factor columns of non-negligible magnitude. Following a block successive upper bound minimization (BSUM) approach for the proposed optimization problem is shown to result in an alternating hierarchical iteratively reweighted least squares (HIRLS) algorithm, which is fast converging and enjoys high computational efficiency, as it relies in its iterations on small-sized sub-problems with closed-form solutions. Simulation results for both synthetic examples and a hyper-spectral image denoising application are reported, which demonstrate the superiority of the proposed scheme over the state-of-the-art in terms of success rate in rank estimation as well as computation time and rate of convergence while attaining a comparable tensor approximation performance.
Keyword:
Tensors
Computational modeling
Estimation
Matrix decomposition
Upper bound
Simulation
Convergence
Alternating group lasso (AGL)
alternating least squares (ALS)
block coordinate descent (BCD)
block successive upper bound minimization (BSUM)
block-term tensor decomposition (BTD)
hierarchical iterative reweighted least squares (HIRLS)
rank
tensor
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