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A novel tensor tracking algorithm for block-term decomposition of streaming tensors
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  • Le Trung Thanh ,
  • karim abed-meraim ,
  • Philippe Ravier ,
  • Olivier Buttelli
Le Trung Thanh
Univeristy of Orleans

Corresponding Author:[email protected]

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karim abed-meraim
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Philippe Ravier
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Olivier Buttelli
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Abstract

Abstract: Block-term decomposition (BTD), which factorizes a tensor (aka a multiway array) into block components of low rank, has been a powerful processing tool for multivariate and high-dimensional data analysis. In this paper, we propose a novel tensor tracking method called SBTD for factorizing tensors derived from multidimensional data streams under the BTD format. Thanks to the alternating optimization framework, SBTD first applies a regularized least-squares solver to estimate the temporal factor of the underlying streaming tensor. Then, SBTD adopts an adaptive filter to track the non-temporal tensor factors over time by minimizing a weighted least-squares cost function. Numerical experiments indicate that SBTD is capable of tensor tracking with competitive performance compared to the state-of-the-art BTD algorithms.
Accepted at IEEE SSP 2023.