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Deep two-way matrix reordering for relational data analysis.

Chihiro Watanabe1, Taiji Suzuki2

  • 1Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|December 17, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces DeepTMR, a novel neural network for matrix reordering. DeepTMR automatically discovers structural patterns without prior knowledge, enhancing data interpretability.

Keywords:
Matrix reorderingNeural networkRelational data analysisVisualization

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Area of Science:

  • Computational mathematics
  • Machine learning
  • Data analysis

Background:

  • Matrix reordering aims to reveal structural patterns by permuting rows and columns.
  • Existing methods often rely on predefined feature extraction, limiting applicability when patterns are unknown.

Purpose of the Study:

  • To develop a novel matrix reordering method that automatically learns feature representations.
  • To address the challenge of matrix reordering without prior knowledge of structural patterns.

Main Methods:

  • A neural network model, DeepTMR (Deep Two-way Matrix Reordering), was developed.
  • The model automatically extracts nonlinear row and column features for reordering.
  • The network also outputs a denoised mean matrix for global structure visualization.

Main Results:

  • DeepTMR effectively performs matrix reordering by learning intrinsic data structures.
  • The method successfully identified meaningful patterns in both synthetic and practical datasets.
  • The denoised mean matrix aids in visualizing the overall structure of the reordered data.

Conclusions:

  • DeepTMR offers an effective, data-driven approach to matrix reordering.
  • The method overcomes limitations of techniques requiring prior structural knowledge.
  • DeepTMR enhances the interpretability of complex matrices through automatic feature learning.