mlsanityData to 7 Oct 2026

On the Accuracy of Hotelling-Type Tensor Deflation: A Random Tensor Analysis

arXiv 2211.09004 ↗First posted 16 Nov 2022math.ST math.PR stat.MLcited by 1

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Abstract (arXiv)

Leveraging on recent advances in random tensor theory, we consider in this paper a rank-r asymmetric spiked tensor model of the form sumi=1r βi Ai + W where βi≥ 0 and the Ai's are rank-one tensors such that langle Ai, Aj rangle∈ [0, 1] for ineq j, based on which we provide an asymptotic study of Hotelling-type tensor deflation in the large dimensional regime. Specifically, our analysis characterizes the singular values and alignments at each step of the deflation procedure, for asymptotically large tensor dimensions. This can be used to construct consistent estimators of different quantities involved in the underlying problem, such as the signal-to-noise ratios βi or the alignments between the different signal components langle Ai, Aj rangle.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 14 references that are in the map. First posted on arXiv 16 Nov 2022.

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6 of 14 references.

References · 6

  1. 2014A statistical model for tensor PCAcited by 72
  2. 2016Statistical limits of spiked tensor modelscited by 27
  3. 2021A Random Matrix Perspective on Random Tensorscited by 12
  4. 2021On Estimating Rank-One Spiked Tensors in the Presence of Heavy Tailed Errorscited by 10
  5. 2021Long Random Matrices and Tensor Unfoldingcited by 10
  6. 2021When Random Tensors meet Random Matricescited by 10

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