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Scaled Coupled Norms and Coupled Higher-Order Tensor Completion.

Kishan Wimalawarne1, Makoto Yamada2, Hiroshi Mamitsuka3

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Neural Computation
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Summary
This summary is machine-generated.

New coupled norms improve tensor completion by controlling regularization for coupled and uncoupled modes. These enhanced norms offer lower risk bounds for higher-order tensors, validated by simulations and real-world data.

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Area of Science:

  • Data Science
  • Numerical Analysis
  • Machine Learning

Background:

  • Coupled norms offer convex solutions for coupled tensor completion by integrating low-rank tensor norms and matrix trace norms.
  • Existing coupled norms face limitations in controlling regularization for coupled/uncoupled modes and are suboptimal for higher-order tensor couplings.

Discussion:

  • This work introduces a novel method to scale regularization between coupled and uncoupled components, ensuring proper low-rank induction on coupled modes.
  • The study also proposes enhanced coupled norms for higher-order tensors by incorporating the square norm, addressing limitations of previous approaches.

Key Insights:

  • The proposed methods achieve lower excess risk bounds compared to existing coupled norms, as proven by theoretical analysis.
  • Experimental results from simulations and real-world data demonstrate the robustness and effectiveness of the developed techniques.

Outlook:

  • Future research could explore adaptive regularization strategies for dynamic tensor completion tasks.
  • Investigating the application of these enhanced coupled norms in specific domains like recommender systems or signal processing is warranted.